Final Project: Analyzing Relationships between Vehicular Crashes and Speed Cameras
Haley Ventura
GGIS 407 Final Project
#import necessary libraries
%matplotlib inline
import geopandas
import os
import fiona
import pprint
import IPython
from matplotlib import pyplot as plt
import pandas as pd
---------------------------------------------------------------------------
ModuleNotFoundError Traceback (most recent call last)
Cell In[1], line 2
1 #import necessary libraries
----> 2 get_ipython().run_line_magic('matplotlib', 'inline')
3 import geopandas
4 import os
File ~\AppData\Local\Programs\Python\Python312\Lib\site-packages\IPython\core\interactiveshell.py:2480, in InteractiveShell.run_line_magic(self, magic_name, line, _stack_depth)
2478 kwargs['local_ns'] = self.get_local_scope(stack_depth)
2479 with self.builtin_trap:
-> 2480 result = fn(*args, **kwargs)
2482 # The code below prevents the output from being displayed
2483 # when using magics with decorator @output_can_be_silenced
2484 # when the last Python token in the expression is a ';'.
2485 if getattr(fn, magic.MAGIC_OUTPUT_CAN_BE_SILENCED, False):
File ~\AppData\Local\Programs\Python\Python312\Lib\site-packages\IPython\core\magics\pylab.py:99, in PylabMagics.matplotlib(self, line)
97 print("Available matplotlib backends: %s" % backends_list)
98 else:
---> 99 gui, backend = self.shell.enable_matplotlib(args.gui.lower() if isinstance(args.gui, str) else args.gui)
100 self._show_matplotlib_backend(args.gui, backend)
File ~\AppData\Local\Programs\Python\Python312\Lib\site-packages\IPython\core\interactiveshell.py:3658, in InteractiveShell.enable_matplotlib(self, gui)
3637 """Enable interactive matplotlib and inline figure support.
3638
3639 This takes the following steps:
(...)
3655 display figures inline.
3656 """
3657 from IPython.core import pylabtools as pt
-> 3658 gui, backend = pt.find_gui_and_backend(gui, self.pylab_gui_select)
3660 if gui != 'inline':
3661 # If we have our first gui selection, store it
3662 if self.pylab_gui_select is None:
File ~\AppData\Local\Programs\Python\Python312\Lib\site-packages\IPython\core\pylabtools.py:320, in find_gui_and_backend(gui, gui_select)
303 def find_gui_and_backend(gui=None, gui_select=None):
304 """Given a gui string return the gui and mpl backend.
305
306 Parameters
(...)
317 'WXAgg','Qt4Agg','module://matplotlib_inline.backend_inline','agg').
318 """
--> 320 import matplotlib
322 has_unified_qt_backend = getattr(matplotlib, "__version_info__", (0, 0)) >= (3, 5)
324 backends_ = dict(backends)
ModuleNotFoundError: No module named 'matplotlib'
#always check for location of files to ensure proper setup
TrafficCrashes = 'TrafficCrashes.shp'
if os.path.exists(TrafficCrashes):
print('TrafficCrashes:', TrafficCrashes)
else:
print('Please download the tutorial data or fix the path!')
TrafficCrashes: TrafficCrashes.shp
#read file
TrafficCrashes = geopandas.read_file(r"TrafficCrashes.shp")
#preview the data. Data is for 2022 only.
TrafficCrashes.head()
| crash_reco | date_crash | time_crash | posted_spe | first_cras | trafficway | crash_type | hit_and_ru | damage | prim_contr | ... | injuries_t | injuries_f | injuries_i | injuries_n | injuries_r | injuries_2 | injuries_u | latitude | longitude | geometry | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 367490fad73ad47e7c570928f719c506c645fbf2099e14... | 2022-12-31 | 23:59:00.000 | 20.0 | PARKED MOTOR VEHICLE | ONE-WAY | NO INJURY / DRIVE AWAY | Y | OVER $1,500 | UNABLE TO DETERMINE | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 41.937900 | -87.792173 | POINT (-87.79217 41.93790) |
| 1 | 538cc213046ff7600cd3586a2ddd41c5b48f76f6ff5225... | 2022-12-31 | 23:55:00.000 | 20.0 | PARKED MOTOR VEHICLE | UNKNOWN | INJURY AND / OR TOW DUE TO CRASH | Y | OVER $1,500 | UNABLE TO DETERMINE | ... | 1.0 | 0.0 | 0.0 | 1.0 | 0.0 | 1.0 | 0.0 | 41.912074 | -87.775463 | POINT (-87.77546 41.91207) |
| 2 | c05d09cfbb00c98d0890811b21cec65fefef70900c1d60... | 2022-12-31 | 23:52:00.000 | 15.0 | PARKED MOTOR VEHICLE | NOT DIVIDED | NO INJURY / DRIVE AWAY | None | $501 - $1,500 | UNABLE TO DETERMINE | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 6.0 | 0.0 | 41.962269 | -87.634734 | POINT (-87.63473 41.96227) |
| 3 | 5e5667bedce936fb6768c48cd1d1d71af618061fe0e476... | 2022-12-31 | 23:45:00.000 | 30.0 | PARKED MOTOR VEHICLE | NOT DIVIDED | NO INJURY / DRIVE AWAY | Y | OVER $1,500 | UNABLE TO DETERMINE | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 41.744200 | -87.566268 | POINT (-87.56627 41.74420) |
| 4 | 70741202005e9dc65235b75cc964f49922cf0b2739a70e... | 2022-12-31 | 23:45:00.000 | 30.0 | FIXED OBJECT | DIVIDED - W/MEDIAN BARRIER | NO INJURY / DRIVE AWAY | Y | OVER $1,500 | NOT APPLICABLE | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 41.732755 | -87.643378 | POINT (-87.64338 41.73276) |
5 rows × 27 columns
#learn about the data!
counts_per_day=TrafficCrashes.groupby('date_crash').size()
counts_per_daysummary= counts_per_day.sort_values(axis=0, ascending=False).head(20)
print(counts_per_daysummary)
date_crash
2022-12-09 456
2022-11-18 441
2022-10-25 437
2022-09-16 437
2022-05-13 432
2022-10-07 412
2022-11-15 405
2022-02-17 404
2022-05-06 400
2022-10-14 392
2022-09-02 391
2022-06-17 389
2022-12-16 388
2022-03-18 387
2022-09-30 385
2022-12-14 384
2022-10-11 379
2022-06-03 379
2022-06-16 377
2022-06-14 376
dtype: int64
#WOW! That's a lot of crashes
averagecrashday=counts_per_day.mean(axis=0)
print("The average number of vehicular crashes each day in Chicago in 2022 was %.f" % averagecrashday)
The average number of vehicular crashes each day in Chicago in 2022 was 294
#map the crashes
TrafficCrashes.plot(markersize=1, figsize=(14,12))
<AxesSubplot:>
#based on the map above, we are not able to conclude much. there are a lot of dots and there are some areas, such as the loop and general grid of the city that are more solid.
#in order to see which areas have more crashes, we add the shapefile with ward boundaries
WardBoundaries = 'WardBoundaries.shp'
if os.path.exists(WardBoundaries):
print('Ward Boundaries:', WardBoundaries)
else:
print('Please download the tutorial data or fix the path!')
Ward Boundaries: WardBoundaries.shp
#read file
WardBoundaries = geopandas.read_file(r"WardBoundaries.shp")
#preview the data
WardBoundaries.head()
| ward | objectid | date_edit_ | time_edit_ | ward_id | globalid | st_area_sh | st_length_ | geometry | |
|---|---|---|---|---|---|---|---|---|---|
| 0 | 1.0 | 51.0 | 2022-06-01 | 00:00:00.000 | 1.0 | {DB2A2A7D-FAF1-42A4-B061-AE18C31A80BB} | 6.589346e+07 | 61878.821587 | POLYGON ((-87.68777 41.92858, -87.68776 41.928... |
| 1 | 2.0 | 52.0 | 2022-06-01 | 00:00:00.000 | 2.0 | {88F300F6-D6DF-4337-8DE3-0C2D27A5B338} | 3.128511e+07 | 74175.949239 | POLYGON ((-87.62517 41.90400, -87.62500 41.904... |
| 2 | 3.0 | 53.0 | 2022-06-01 | 00:00:00.000 | 3.0 | {4F169974-0F93-4C35-98C1-20A8415C72F5} | 9.777341e+07 | 85625.597620 | POLYGON ((-87.61992 41.86577, -87.61985 41.865... |
| 3 | 4.0 | 54.0 | 2022-06-01 | 00:00:00.000 | 4.0 | {02DC7BBB-BD1E-413C-994E-0A3154FD3D54} | 1.158091e+08 | 103594.867171 | POLYGON ((-87.62429 41.87700, -87.62429 41.876... |
| 4 | 5.0 | 55.0 | 2022-06-01 | 00:00:00.000 | 5.0 | {0A109A41-9DED-47D7-934E-1EA1CC7EE025} | 1.120803e+08 | 88207.690241 | POLYGON ((-87.56030 41.76636, -87.56058 41.766... |
#review the data based on shape area. The wards are all different sizes, so this will affect our results.
#A ward with a larger geographic area is likely to have more crashes, but that does not mean the frequency of crashes is worse.
WardBoundaries.sort_values(by=['st_area_sh'],axis=0, ascending=False)
| ward | objectid | date_edit_ | time_edit_ | ward_id | globalid | st_area_sh | st_length_ | geometry | |
|---|---|---|---|---|---|---|---|---|---|
| 9 | 10.0 | 60.0 | 2022-06-01 | 00:00:00.000 | 10.0 | {19FF2026-6DDD-45DA-8DF6-B44B0C15834C} | 5.773395e+08 | 147455.404991 | POLYGON ((-87.53018 41.74310, -87.53008 41.742... |
| 40 | 41.0 | 91.0 | 2022-06-01 | 00:00:00.000 | 41.0 | {C885247A-E064-4FCF-A35A-747DEC142421} | 4.754967e+08 | 218605.417697 | POLYGON ((-87.79065 42.00040, -87.79058 42.000... |
| 8 | 9.0 | 59.0 | 2022-06-01 | 00:00:00.000 | 9.0 | {B75B90E6-CB95-4890-BBC3-D2F313E2EE80} | 2.344895e+08 | 102227.294779 | POLYGON ((-87.61414 41.72189, -87.61414 41.721... |
| 18 | 19.0 | 69.0 | 2022-06-01 | 00:00:00.000 | 19.0 | {91001AF5-B4E0-4F45-A1CB-7C22070BA22D} | 2.199899e+08 | 101405.125517 | POLYGON ((-87.66674 41.73022, -87.66677 41.730... |
| 20 | 21.0 | 71.0 | 2022-06-01 | 00:00:00.000 | 21.0 | {BA7C81E8-7DEB-46DF-8E71-95F9AE112FF3} | 2.118767e+08 | 92642.006031 | POLYGON ((-87.62405 41.73014, -87.62410 41.730... |
| 17 | 18.0 | 68.0 | 2022-06-01 | 00:00:00.000 | 18.0 | {E22B6995-91F8-4C49-A9F3-4085D503F2A4} | 1.933789e+08 | 79337.751930 | POLYGON ((-87.65830 41.73950, -87.65834 41.739... |
| 38 | 39.0 | 89.0 | 2022-06-01 | 00:00:00.000 | 39.0 | {15480A27-2480-40CF-91EC-72B090F03522} | 1.818672e+08 | 77704.757487 | POLYGON ((-87.69916 41.98312, -87.69915 41.982... |
| 7 | 8.0 | 58.0 | 2022-06-01 | 00:00:00.000 | 8.0 | {2B5945BD-349C-4088-9B25-D99D4FB6C01D} | 1.799926e+08 | 77255.568224 | POLYGON ((-87.58587 41.76424, -87.58597 41.764... |
| 37 | 38.0 | 88.0 | 2022-06-01 | 00:00:00.000 | 38.0 | {6E5F0557-2DCA-490E-8E7D-C5B58CA65DFA} | 1.775660e+08 | 91571.573668 | POLYGON ((-87.75961 41.95327, -87.75964 41.953... |
| 27 | 28.0 | 78.0 | 2022-06-01 | 00:00:00.000 | 28.0 | {65BDECD2-AB03-4F1B-8025-49E3B1029A49} | 1.600439e+08 | 129592.820270 | POLYGON ((-87.72098 41.88802, -87.71974 41.888... |
| 26 | 27.0 | 77.0 | 2022-06-01 | 00:00:00.000 | 27.0 | {5EFBE372-72FB-4702-8C3C-BA882825EEF1} | 1.595750e+08 | 129446.885360 | POLYGON ((-87.63420 41.89660, -87.63426 41.896... |
| 12 | 13.0 | 63.0 | 2022-06-01 | 00:00:00.000 | 13.0 | {883D9536-9DDE-4FB8-B340-E9A644FD1851} | 1.480001e+08 | 59975.600111 | POLYGON ((-87.74205 41.77113, -87.74206 41.771... |
| 19 | 20.0 | 70.0 | 2022-06-01 | 00:00:00.000 | 20.0 | {1EA93731-81AC-42D6-9711-283C3A3CA2BE} | 1.453499e+08 | 100221.251179 | POLYGON ((-87.62082 41.79383, -87.62050 41.793... |
| 6 | 7.0 | 57.0 | 2022-06-01 | 00:00:00.000 | 7.0 | {279FCBD9-EA0D-4FFC-A8CA-EDA2676C0721} | 1.414924e+08 | 98906.567862 | POLYGON ((-87.54393 41.76030, -87.54370 41.760... |
| 44 | 45.0 | 95.0 | 2022-06-01 | 00:00:00.000 | 45.0 | {18480250-3E08-4D68-BF2E-96E27F6503B5} | 1.394346e+08 | 117115.300022 | POLYGON ((-87.77310 42.01148, -87.77309 42.011... |
| 5 | 6.0 | 56.0 | 2022-06-01 | 00:00:00.000 | 6.0 | {FD74A999-4BBA-4CE3-BEBB-CC76423037E8} | 1.392022e+08 | 80779.851890 | POLYGON ((-87.61794 41.77292, -87.61786 41.772... |
| 11 | 12.0 | 62.0 | 2022-06-01 | 00:00:00.000 | 12.0 | {EAD019CB-AA66-48C4-9338-795B4E083B59} | 1.334616e+08 | 76139.484398 | POLYGON ((-87.66588 41.84493, -87.66581 41.844... |
| 16 | 17.0 | 67.0 | 2022-06-01 | 00:00:00.000 | 17.0 | {A0475DAB-473E-447B-9F82-AEC0CA483C93} | 1.301704e+08 | 90154.781522 | POLYGON ((-87.65421 41.76869, -87.65421 41.768... |
| 10 | 11.0 | 61.0 | 2022-06-01 | 00:00:00.000 | 11.0 | {C7397C87-C4AE-4C48-A00E-50D2CD1100CF} | 1.273484e+08 | 64972.869726 | POLYGON ((-87.63600 41.81635, -87.63602 41.816... |
| 15 | 16.0 | 66.0 | 2022-06-01 | 00:00:00.000 | 16.0 | {B54690B6-21DF-44F5-B898-D73EF9976BDC} | 1.258524e+08 | 90382.506038 | POLYGON ((-87.64077 41.77616, -87.64082 41.776... |
| 13 | 14.0 | 64.0 | 2022-06-01 | 00:00:00.000 | 14.0 | {612295DC-E128-468C-B0C9-A1ADE1CB1C84} | 1.213109e+08 | 72296.250663 | POLYGON ((-87.69421 41.80828, -87.69420 41.808... |
| 28 | 29.0 | 79.0 | 2022-06-01 | 00:00:00.000 | 29.0 | {C1A9937E-2864-4CFB-ADD5-B8397C56DC77} | 1.193660e+08 | 97816.079177 | POLYGON ((-87.80127 41.93944, -87.80127 41.939... |
| 22 | 23.0 | 73.0 | 2022-06-01 | 00:00:00.000 | 23.0 | {51BB999E-306A-4091-8F7E-4241AACA3340} | 1.168305e+08 | 94602.226753 | POLYGON ((-87.71024 41.77156, -87.71039 41.771... |
| 3 | 4.0 | 54.0 | 2022-06-01 | 00:00:00.000 | 4.0 | {02DC7BBB-BD1E-413C-994E-0A3154FD3D54} | 1.158091e+08 | 103594.867171 | POLYGON ((-87.62429 41.87700, -87.62429 41.876... |
| 4 | 5.0 | 55.0 | 2022-06-01 | 00:00:00.000 | 5.0 | {0A109A41-9DED-47D7-934E-1EA1CC7EE025} | 1.120803e+08 | 88207.690241 | POLYGON ((-87.56030 41.76636, -87.56058 41.766... |
| 23 | 24.0 | 74.0 | 2022-06-01 | 00:00:00.000 | 24.0 | {9ED052F7-F703-4A2D-A829-363FB60472C4} | 1.120000e+08 | 68816.597232 | POLYGON ((-87.69070 41.85635, -87.69077 41.856... |
| 21 | 22.0 | 72.0 | 2022-06-01 | 00:00:00.000 | 22.0 | {0705D4DE-80BF-473A-B3B7-078D63AD0298} | 1.055096e+08 | 60413.987154 | POLYGON ((-87.70761 41.84995, -87.70761 41.849... |
| 36 | 37.0 | 87.0 | 2022-06-01 | 00:00:00.000 | 37.0 | {35FCA85E-7EA3-4399-9077-7EACCF85268E} | 1.004835e+08 | 56306.600969 | POLYGON ((-87.72266 41.90316, -87.72266 41.903... |
| 2 | 3.0 | 53.0 | 2022-06-01 | 00:00:00.000 | 3.0 | {4F169974-0F93-4C35-98C1-20A8415C72F5} | 9.777341e+07 | 85625.597620 | POLYGON ((-87.61992 41.86577, -87.61985 41.865... |
| 39 | 40.0 | 90.0 | 2022-06-01 | 00:00:00.000 | 40.0 | {EA278E33-45CB-4D28-BEE1-F64C155E65B9} | 9.664484e+07 | 67854.255079 | POLYGON ((-87.68152 42.00812, -87.68144 42.007... |
| 29 | 30.0 | 80.0 | 2022-06-01 | 00:00:00.000 | 30.0 | {15C33223-51B6-456F-B790-BEC795885C33} | 9.490456e+07 | 81414.586459 | POLYGON ((-87.73242 41.95181, -87.73242 41.951... |
| 24 | 25.0 | 75.0 | 2022-06-01 | 00:00:00.000 | 25.0 | {B1A8F67E-3BEB-4E6A-967B-DB2271B162C5} | 9.329780e+07 | 80678.887175 | POLYGON ((-87.63901 41.86002, -87.63901 41.859... |
| 31 | 32.0 | 82.0 | 2022-06-01 | 00:00:00.000 | 32.0 | {2106D44D-07AC-4B39-A878-BEBB439D1BF3} | 9.288828e+07 | 47973.924224 | POLYGON ((-87.68097 41.94636, -87.68097 41.946... |
| 46 | 47.0 | 97.0 | 2022-06-01 | 00:00:00.000 | 47.0 | {4F17B913-08A1-4EF6-BF20-0EF4B6478494} | 8.481029e+07 | 55157.008247 | POLYGON ((-87.65979 41.97332, -87.65978 41.973... |
| 35 | 36.0 | 86.0 | 2022-06-01 | 00:00:00.000 | 36.0 | {337BAF17-7684-4F7F-BFCA-0BFD2B92B869} | 8.383845e+07 | 118667.485047 | POLYGON ((-87.76599 41.92398, -87.76591 41.923... |
| 14 | 15.0 | 65.0 | 2022-06-01 | 00:00:00.000 | 15.0 | {51F05987-1A65-4707-B17E-98C8709061B1} | 8.307862e+07 | 107657.780595 | POLYGON ((-87.63584 41.80905, -87.63585 41.809... |
| 25 | 26.0 | 76.0 | 2022-06-01 | 00:00:00.000 | 26.0 | {BB18C0C9-4A3A-43F8-A953-6A8B4D2487D5} | 7.767961e+07 | 60930.848090 | POLYGON ((-87.73649 41.92253, -87.73648 41.922... |
| 34 | 35.0 | 85.0 | 2022-06-01 | 00:00:00.000 | 35.0 | {3FDB81BF-D813-4AA7-9F92-DD74FD23B646} | 7.613137e+07 | 70987.979595 | POLYGON ((-87.69620 41.94670, -87.69620 41.946... |
| 49 | 50.0 | 100.0 | 2022-06-01 | 00:00:00.000 | 50.0 | {C4F10402-10E2-4401-BD4D-7FBDAF35E3FC} | 7.450479e+07 | 60789.219509 | POLYGON ((-87.68189 42.00896, -87.68204 42.008... |
| 30 | 31.0 | 81.0 | 2022-06-01 | 00:00:00.000 | 31.0 | {0E9C61BE-C5B1-4051-AB5C-833CB7887482} | 7.442608e+07 | 52347.368005 | POLYGON ((-87.72392 41.93913, -87.72371 41.939... |
| 0 | 1.0 | 51.0 | 2022-06-01 | 00:00:00.000 | 1.0 | {DB2A2A7D-FAF1-42A4-B061-AE18C31A80BB} | 6.589346e+07 | 61878.821587 | POLYGON ((-87.68777 41.92858, -87.68776 41.928... |
| 42 | 43.0 | 93.0 | 2022-06-01 | 00:00:00.000 | 43.0 | {1003F8BC-CAE5-46E7-8A50-95CC5B60E8C7} | 6.223707e+07 | 48459.476947 | POLYGON ((-87.63183 41.93305, -87.63183 41.932... |
| 32 | 33.0 | 83.0 | 2022-06-01 | 00:00:00.000 | 33.0 | {F01E135A-54DB-49B2-867E-7F2DCD35A1F6} | 6.217322e+07 | 48801.435311 | POLYGON ((-87.70353 41.97186, -87.70344 41.971... |
| 45 | 46.0 | 96.0 | 2022-06-01 | 00:00:00.000 | 46.0 | {C894F602-BEBF-4984-803F-736F2BB8F6DE} | 5.592727e+07 | 52699.506856 | POLYGON ((-87.64521 41.96974, -87.64516 41.969... |
| 41 | 42.0 | 92.0 | 2022-06-01 | 00:00:00.000 | 42.0 | {AE139837-FCC1-4E2A-84A3-15B2B67D65A4} | 5.253453e+07 | 68484.113474 | POLYGON ((-87.62685 41.89668, -87.62685 41.896... |
| 48 | 49.0 | 99.0 | 2022-06-01 | 00:00:00.000 | 49.0 | {F7B2FFFC-D9B6-463F-BFF0-8C5C2ED72D47} | 5.088363e+07 | 37398.359952 | POLYGON ((-87.65729 42.00418, -87.65729 42.004... |
| 43 | 44.0 | 94.0 | 2022-06-01 | 00:00:00.000 | 44.0 | {E181C590-6E28-4CF9-85A1-96AF19075184} | 4.750785e+07 | 36707.423435 | POLYGON ((-87.65682 41.95140, -87.65681 41.951... |
| 47 | 48.0 | 98.0 | 2022-06-01 | 00:00:00.000 | 48.0 | {174DEC8E-5281-4774-831E-A7E0F6F3440D} | 4.500890e+07 | 35618.125393 | POLYGON ((-87.64430 41.96990, -87.64516 41.969... |
| 33 | 34.0 | 84.0 | 2022-06-01 | 00:00:00.000 | 34.0 | {54EBC7A4-FE32-4936-A72D-33A39D37248F} | 4.272862e+07 | 75371.506020 | POLYGON ((-87.63968 41.88906, -87.63924 41.887... |
| 1 | 2.0 | 52.0 | 2022-06-01 | 00:00:00.000 | 2.0 | {88F300F6-D6DF-4337-8DE3-0C2D27A5B338} | 3.128511e+07 | 74175.949239 | POLYGON ((-87.62517 41.90400, -87.62500 41.904... |
#map the wards. Based on my previous knowledge of Chicago, ward size is mainly based on population.
#ward shape is very irregular due to political motivations
WardBoundaries.plot(figsize=(14,12))
<AxesSubplot:>
#ensure the coordinate system is the same before joining the files. We must join the files to see crashes within each ward.
WardBoundaries.crs
<Geographic 2D CRS: EPSG:4326>
Name: WGS 84
Axis Info [ellipsoidal]:
- Lat[north]: Geodetic latitude (degree)
- Lon[east]: Geodetic longitude (degree)
Area of Use:
- name: World.
- bounds: (-180.0, -90.0, 180.0, 90.0)
Datum: World Geodetic System 1984 ensemble
- Ellipsoid: WGS 84
- Prime Meridian: Greenwich
TrafficCrashes.crs
<Geographic 2D CRS: EPSG:4326>
Name: WGS 84
Axis Info [ellipsoidal]:
- Lat[north]: Geodetic latitude (degree)
- Lon[east]: Geodetic longitude (degree)
Area of Use:
- name: World.
- bounds: (-180.0, -90.0, 180.0, 90.0)
Datum: World Geodetic System 1984 ensemble
- Ellipsoid: WGS 84
- Prime Meridian: Greenwich
#join ward to crashes so we can see what ward every crash is within
crashes_by_ward=geopandas.sjoin(TrafficCrashes,WardBoundaries[['ward','geometry']].copy(),predicate='within')
#preview the data to ensure the join operated as we anticipated it would
crashes_by_ward.head()
| crash_reco | date_crash | time_crash | posted_spe | first_cras | trafficway | crash_type | hit_and_ru | damage | prim_contr | ... | injuries_i | injuries_n | injuries_r | injuries_2 | injuries_u | latitude | longitude | geometry | index_right | ward | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 367490fad73ad47e7c570928f719c506c645fbf2099e14... | 2022-12-31 | 23:59:00.000 | 20.0 | PARKED MOTOR VEHICLE | ONE-WAY | NO INJURY / DRIVE AWAY | Y | OVER $1,500 | UNABLE TO DETERMINE | ... | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 41.937900 | -87.792173 | POINT (-87.79217 41.93790) | 35 | 36.0 |
| 73 | 4e472536a1fb6f72ffcc851db6795c29db404862d1b9a1... | 2022-12-31 | 19:04:00.000 | 25.0 | ANGLE | FOUR WAY | INJURY AND / OR TOW DUE TO CRASH | Y | OVER $1,500 | UNABLE TO DETERMINE | ... | 0.0 | 1.0 | 0.0 | 2.0 | 0.0 | 41.936562 | -87.787244 | POINT (-87.78724 41.93656) | 35 | 36.0 |
| 195 | 9b18219e9cf780d90b6fa9a54eb5800ca44ddfd0e361d2... | 2022-12-31 | 11:25:00.000 | 25.0 | REAR TO SIDE | PARKING LOT | NO INJURY / DRIVE AWAY | None | $501 - $1,500 | UNABLE TO DETERMINE | ... | 0.0 | 0.0 | 0.0 | 2.0 | 0.0 | 41.911946 | -87.736321 | POINT (-87.73632 41.91195) | 35 | 36.0 |
| 207 | c804f78acd5c9ba0688d3f85f3eacc1fb6462696a8f178... | 2022-12-31 | 09:50:00.000 | 30.0 | TURNING | NOT DIVIDED | NO INJURY / DRIVE AWAY | None | OVER $1,500 | IMPROPER TURNING/NO SIGNAL | ... | 0.0 | 0.0 | 0.0 | 2.0 | 0.0 | 41.923870 | -87.766140 | POINT (-87.76614 41.92387) | 35 | 36.0 |
| 224 | a3c8a19eab9b4d958c12394e462dfb2161dcc58df9f136... | 2022-12-31 | 06:45:00.000 | 30.0 | PEDALCYCLIST | NOT DIVIDED | INJURY AND / OR TOW DUE TO CRASH | None | $501 - $1,500 | FAILING TO YIELD RIGHT-OF-WAY | ... | 0.0 | 0.0 | 1.0 | 1.0 | 0.0 | 41.907685 | -87.729926 | POINT (-87.72993 41.90768) | 35 | 36.0 |
5 rows × 29 columns
#view a summary of how many crashes are in each ward and sort descending by crash number.
counts_per_ward=crashes_by_ward.groupby('ward',as_index=False).size()
counts_per_ward.rename(columns={"size":"number_of_crashes"},inplace=True)
counts_per_ward.sort_values(by='number_of_crashes',axis=0, ascending=False)
| ward | number_of_crashes | |
|---|---|---|
| 27 | 28.0 | 4611 |
| 26 | 27.0 | 4334 |
| 41 | 42.0 | 3886 |
| 5 | 6.0 | 3181 |
| 20 | 21.0 | 3146 |
| 7 | 8.0 | 2876 |
| 23 | 24.0 | 2664 |
| 36 | 37.0 | 2650 |
| 19 | 20.0 | 2505 |
| 15 | 16.0 | 2490 |
| 3 | 4.0 | 2488 |
| 16 | 17.0 | 2461 |
| 28 | 29.0 | 2413 |
| 13 | 14.0 | 2322 |
| 11 | 12.0 | 2300 |
| 17 | 18.0 | 2261 |
| 31 | 32.0 | 2228 |
| 33 | 34.0 | 2200 |
| 8 | 9.0 | 2129 |
| 35 | 36.0 | 2126 |
| 44 | 45.0 | 2114 |
| 24 | 25.0 | 2113 |
| 4 | 5.0 | 2101 |
| 22 | 23.0 | 2082 |
| 2 | 3.0 | 2058 |
| 1 | 2.0 | 2023 |
| 38 | 39.0 | 1934 |
| 30 | 31.0 | 1934 |
| 12 | 13.0 | 1889 |
| 21 | 22.0 | 1843 |
| 0 | 1.0 | 1815 |
| 18 | 19.0 | 1790 |
| 34 | 35.0 | 1772 |
| 29 | 30.0 | 1768 |
| 14 | 15.0 | 1762 |
| 40 | 41.0 | 1728 |
| 10 | 11.0 | 1702 |
| 6 | 7.0 | 1688 |
| 49 | 50.0 | 1685 |
| 37 | 38.0 | 1654 |
| 9 | 10.0 | 1629 |
| 39 | 40.0 | 1604 |
| 25 | 26.0 | 1568 |
| 46 | 47.0 | 1554 |
| 42 | 43.0 | 1453 |
| 43 | 44.0 | 1402 |
| 32 | 33.0 | 1395 |
| 47 | 48.0 | 1380 |
| 45 | 46.0 | 1349 |
| 48 | 49.0 | 1293 |
These results are very interesting. I took a look at the detailed maps of each ward ( https://www.chicago.gov/city/en/depts/dgs/supp_info/ward_maps.html ) and noticed that the top 3, which were far above the other wards, each contained large portions of the Eisenhower highway (I-290), Kennedy highway (I-90), and Dusable Lakeshore Drive (DLSD). In comparison, the bottom three wards are geographically smaller due to higher density of population and only 2 of the 3 had portions of DLSD, and they are typically not as congested sections.
#add this summary to the ward data so we have a simiplified file that lists the number of crashes in each ward with the ward shapefile information. Preview the data.
Wards = WardBoundaries.merge(counts_per_ward, on='ward')
Wards.head()
| ward | objectid | date_edit_ | time_edit_ | ward_id | globalid | st_area_sh | st_length_ | geometry | number_of_crashes | |
|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1.0 | 51.0 | 2022-06-01 | 00:00:00.000 | 1.0 | {DB2A2A7D-FAF1-42A4-B061-AE18C31A80BB} | 6.589346e+07 | 61878.821587 | POLYGON ((-87.68777 41.92858, -87.68776 41.928... | 1815 |
| 1 | 2.0 | 52.0 | 2022-06-01 | 00:00:00.000 | 2.0 | {88F300F6-D6DF-4337-8DE3-0C2D27A5B338} | 3.128511e+07 | 74175.949239 | POLYGON ((-87.62517 41.90400, -87.62500 41.904... | 2023 |
| 2 | 3.0 | 53.0 | 2022-06-01 | 00:00:00.000 | 3.0 | {4F169974-0F93-4C35-98C1-20A8415C72F5} | 9.777341e+07 | 85625.597620 | POLYGON ((-87.61992 41.86577, -87.61985 41.865... | 2058 |
| 3 | 4.0 | 54.0 | 2022-06-01 | 00:00:00.000 | 4.0 | {02DC7BBB-BD1E-413C-994E-0A3154FD3D54} | 1.158091e+08 | 103594.867171 | POLYGON ((-87.62429 41.87700, -87.62429 41.876... | 2488 |
| 4 | 5.0 | 55.0 | 2022-06-01 | 00:00:00.000 | 5.0 | {0A109A41-9DED-47D7-934E-1EA1CC7EE025} | 1.120803e+08 | 88207.690241 | POLYGON ((-87.56030 41.76636, -87.56058 41.766... | 2101 |
#plot the data as a choropleth map to show the range of crashes and where there are the most.
ax=Wards.plot(column='number_of_crashes', figsize=(14,12),cmap='OrRd', legend=True)
#The issue here is that the wards are all different sizes and we cannot exactly compare them one for one.
#I am interested in this comparison to know density of crashes and be able to target wards that need political intervention.
#I will add a column that normalizes the crashes by area
Wards["crashes_per_area"]=Wards["number_of_crashes"] / Wards["st_area_sh"]
Wards.sort_values(by='crashes_per_area',ascending=False)
| ward | objectid | date_edit_ | time_edit_ | ward_id | globalid | st_area_sh | st_length_ | geometry | number_of_crashes | crashes_per_area | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 41 | 42.0 | 92.0 | 2022-06-01 | 00:00:00.000 | 42.0 | {AE139837-FCC1-4E2A-84A3-15B2B67D65A4} | 5.253453e+07 | 68484.113474 | POLYGON ((-87.62685 41.89668, -87.62685 41.896... | 3886 | 0.000074 |
| 1 | 2.0 | 52.0 | 2022-06-01 | 00:00:00.000 | 2.0 | {88F300F6-D6DF-4337-8DE3-0C2D27A5B338} | 3.128511e+07 | 74175.949239 | POLYGON ((-87.62517 41.90400, -87.62500 41.904... | 2023 | 0.000065 |
| 33 | 34.0 | 84.0 | 2022-06-01 | 00:00:00.000 | 34.0 | {54EBC7A4-FE32-4936-A72D-33A39D37248F} | 4.272862e+07 | 75371.506020 | POLYGON ((-87.63968 41.88906, -87.63924 41.887... | 2200 | 0.000051 |
| 47 | 48.0 | 98.0 | 2022-06-01 | 00:00:00.000 | 48.0 | {174DEC8E-5281-4774-831E-A7E0F6F3440D} | 4.500890e+07 | 35618.125393 | POLYGON ((-87.64430 41.96990, -87.64516 41.969... | 1380 | 0.000031 |
| 43 | 44.0 | 94.0 | 2022-06-01 | 00:00:00.000 | 44.0 | {E181C590-6E28-4CF9-85A1-96AF19075184} | 4.750785e+07 | 36707.423435 | POLYGON ((-87.65682 41.95140, -87.65681 41.951... | 1402 | 0.000030 |
| 27 | 28.0 | 78.0 | 2022-06-01 | 00:00:00.000 | 28.0 | {65BDECD2-AB03-4F1B-8025-49E3B1029A49} | 1.600439e+08 | 129592.820270 | POLYGON ((-87.72098 41.88802, -87.71974 41.888... | 4611 | 0.000029 |
| 0 | 1.0 | 51.0 | 2022-06-01 | 00:00:00.000 | 1.0 | {DB2A2A7D-FAF1-42A4-B061-AE18C31A80BB} | 6.589346e+07 | 61878.821587 | POLYGON ((-87.68777 41.92858, -87.68776 41.928... | 1815 | 0.000028 |
| 26 | 27.0 | 77.0 | 2022-06-01 | 00:00:00.000 | 27.0 | {5EFBE372-72FB-4702-8C3C-BA882825EEF1} | 1.595750e+08 | 129446.885360 | POLYGON ((-87.63420 41.89660, -87.63426 41.896... | 4334 | 0.000027 |
| 36 | 37.0 | 87.0 | 2022-06-01 | 00:00:00.000 | 37.0 | {35FCA85E-7EA3-4399-9077-7EACCF85268E} | 1.004835e+08 | 56306.600969 | POLYGON ((-87.72266 41.90316, -87.72266 41.903... | 2650 | 0.000026 |
| 30 | 31.0 | 81.0 | 2022-06-01 | 00:00:00.000 | 31.0 | {0E9C61BE-C5B1-4051-AB5C-833CB7887482} | 7.442608e+07 | 52347.368005 | POLYGON ((-87.72392 41.93913, -87.72371 41.939... | 1934 | 0.000026 |
| 48 | 49.0 | 99.0 | 2022-06-01 | 00:00:00.000 | 49.0 | {F7B2FFFC-D9B6-463F-BFF0-8C5C2ED72D47} | 5.088363e+07 | 37398.359952 | POLYGON ((-87.65729 42.00418, -87.65729 42.004... | 1293 | 0.000025 |
| 35 | 36.0 | 86.0 | 2022-06-01 | 00:00:00.000 | 36.0 | {337BAF17-7684-4F7F-BFCA-0BFD2B92B869} | 8.383845e+07 | 118667.485047 | POLYGON ((-87.76599 41.92398, -87.76591 41.923... | 2126 | 0.000025 |
| 45 | 46.0 | 96.0 | 2022-06-01 | 00:00:00.000 | 46.0 | {C894F602-BEBF-4984-803F-736F2BB8F6DE} | 5.592727e+07 | 52699.506856 | POLYGON ((-87.64521 41.96974, -87.64516 41.969... | 1349 | 0.000024 |
| 31 | 32.0 | 82.0 | 2022-06-01 | 00:00:00.000 | 32.0 | {2106D44D-07AC-4B39-A878-BEBB439D1BF3} | 9.288828e+07 | 47973.924224 | POLYGON ((-87.68097 41.94636, -87.68097 41.946... | 2228 | 0.000024 |
| 23 | 24.0 | 74.0 | 2022-06-01 | 00:00:00.000 | 24.0 | {9ED052F7-F703-4A2D-A829-363FB60472C4} | 1.120000e+08 | 68816.597232 | POLYGON ((-87.69070 41.85635, -87.69077 41.856... | 2664 | 0.000024 |
| 42 | 43.0 | 93.0 | 2022-06-01 | 00:00:00.000 | 43.0 | {1003F8BC-CAE5-46E7-8A50-95CC5B60E8C7} | 6.223707e+07 | 48459.476947 | POLYGON ((-87.63183 41.93305, -87.63183 41.932... | 1453 | 0.000023 |
| 34 | 35.0 | 85.0 | 2022-06-01 | 00:00:00.000 | 35.0 | {3FDB81BF-D813-4AA7-9F92-DD74FD23B646} | 7.613137e+07 | 70987.979595 | POLYGON ((-87.69620 41.94670, -87.69620 41.946... | 1772 | 0.000023 |
| 5 | 6.0 | 56.0 | 2022-06-01 | 00:00:00.000 | 6.0 | {FD74A999-4BBA-4CE3-BEBB-CC76423037E8} | 1.392022e+08 | 80779.851890 | POLYGON ((-87.61794 41.77292, -87.61786 41.772... | 3181 | 0.000023 |
| 24 | 25.0 | 75.0 | 2022-06-01 | 00:00:00.000 | 25.0 | {B1A8F67E-3BEB-4E6A-967B-DB2271B162C5} | 9.329780e+07 | 80678.887175 | POLYGON ((-87.63901 41.86002, -87.63901 41.859... | 2113 | 0.000023 |
| 49 | 50.0 | 100.0 | 2022-06-01 | 00:00:00.000 | 50.0 | {C4F10402-10E2-4401-BD4D-7FBDAF35E3FC} | 7.450479e+07 | 60789.219509 | POLYGON ((-87.68189 42.00896, -87.68204 42.008... | 1685 | 0.000023 |
| 32 | 33.0 | 83.0 | 2022-06-01 | 00:00:00.000 | 33.0 | {F01E135A-54DB-49B2-867E-7F2DCD35A1F6} | 6.217322e+07 | 48801.435311 | POLYGON ((-87.70353 41.97186, -87.70344 41.971... | 1395 | 0.000022 |
| 3 | 4.0 | 54.0 | 2022-06-01 | 00:00:00.000 | 4.0 | {02DC7BBB-BD1E-413C-994E-0A3154FD3D54} | 1.158091e+08 | 103594.867171 | POLYGON ((-87.62429 41.87700, -87.62429 41.876... | 2488 | 0.000021 |
| 14 | 15.0 | 65.0 | 2022-06-01 | 00:00:00.000 | 15.0 | {51F05987-1A65-4707-B17E-98C8709061B1} | 8.307862e+07 | 107657.780595 | POLYGON ((-87.63584 41.80905, -87.63585 41.809... | 1762 | 0.000021 |
| 2 | 3.0 | 53.0 | 2022-06-01 | 00:00:00.000 | 3.0 | {4F169974-0F93-4C35-98C1-20A8415C72F5} | 9.777341e+07 | 85625.597620 | POLYGON ((-87.61992 41.86577, -87.61985 41.865... | 2058 | 0.000021 |
| 28 | 29.0 | 79.0 | 2022-06-01 | 00:00:00.000 | 29.0 | {C1A9937E-2864-4CFB-ADD5-B8397C56DC77} | 1.193660e+08 | 97816.079177 | POLYGON ((-87.80127 41.93944, -87.80127 41.939... | 2413 | 0.000020 |
| 25 | 26.0 | 76.0 | 2022-06-01 | 00:00:00.000 | 26.0 | {BB18C0C9-4A3A-43F8-A953-6A8B4D2487D5} | 7.767961e+07 | 60930.848090 | POLYGON ((-87.73649 41.92253, -87.73648 41.922... | 1568 | 0.000020 |
| 15 | 16.0 | 66.0 | 2022-06-01 | 00:00:00.000 | 16.0 | {B54690B6-21DF-44F5-B898-D73EF9976BDC} | 1.258524e+08 | 90382.506038 | POLYGON ((-87.64077 41.77616, -87.64082 41.776... | 2490 | 0.000020 |
| 13 | 14.0 | 64.0 | 2022-06-01 | 00:00:00.000 | 14.0 | {612295DC-E128-468C-B0C9-A1ADE1CB1C84} | 1.213109e+08 | 72296.250663 | POLYGON ((-87.69421 41.80828, -87.69420 41.808... | 2322 | 0.000019 |
| 16 | 17.0 | 67.0 | 2022-06-01 | 00:00:00.000 | 17.0 | {A0475DAB-473E-447B-9F82-AEC0CA483C93} | 1.301704e+08 | 90154.781522 | POLYGON ((-87.65421 41.76869, -87.65421 41.768... | 2461 | 0.000019 |
| 4 | 5.0 | 55.0 | 2022-06-01 | 00:00:00.000 | 5.0 | {0A109A41-9DED-47D7-934E-1EA1CC7EE025} | 1.120803e+08 | 88207.690241 | POLYGON ((-87.56030 41.76636, -87.56058 41.766... | 2101 | 0.000019 |
| 29 | 30.0 | 80.0 | 2022-06-01 | 00:00:00.000 | 30.0 | {15C33223-51B6-456F-B790-BEC795885C33} | 9.490456e+07 | 81414.586459 | POLYGON ((-87.73242 41.95181, -87.73242 41.951... | 1768 | 0.000019 |
| 46 | 47.0 | 97.0 | 2022-06-01 | 00:00:00.000 | 47.0 | {4F17B913-08A1-4EF6-BF20-0EF4B6478494} | 8.481029e+07 | 55157.008247 | POLYGON ((-87.65979 41.97332, -87.65978 41.973... | 1554 | 0.000018 |
| 22 | 23.0 | 73.0 | 2022-06-01 | 00:00:00.000 | 23.0 | {51BB999E-306A-4091-8F7E-4241AACA3340} | 1.168305e+08 | 94602.226753 | POLYGON ((-87.71024 41.77156, -87.71039 41.771... | 2082 | 0.000018 |
| 21 | 22.0 | 72.0 | 2022-06-01 | 00:00:00.000 | 22.0 | {0705D4DE-80BF-473A-B3B7-078D63AD0298} | 1.055096e+08 | 60413.987154 | POLYGON ((-87.70761 41.84995, -87.70761 41.849... | 1843 | 0.000017 |
| 19 | 20.0 | 70.0 | 2022-06-01 | 00:00:00.000 | 20.0 | {1EA93731-81AC-42D6-9711-283C3A3CA2BE} | 1.453499e+08 | 100221.251179 | POLYGON ((-87.62082 41.79383, -87.62050 41.793... | 2505 | 0.000017 |
| 11 | 12.0 | 62.0 | 2022-06-01 | 00:00:00.000 | 12.0 | {EAD019CB-AA66-48C4-9338-795B4E083B59} | 1.334616e+08 | 76139.484398 | POLYGON ((-87.66588 41.84493, -87.66581 41.844... | 2300 | 0.000017 |
| 39 | 40.0 | 90.0 | 2022-06-01 | 00:00:00.000 | 40.0 | {EA278E33-45CB-4D28-BEE1-F64C155E65B9} | 9.664484e+07 | 67854.255079 | POLYGON ((-87.68152 42.00812, -87.68144 42.007... | 1604 | 0.000017 |
| 7 | 8.0 | 58.0 | 2022-06-01 | 00:00:00.000 | 8.0 | {2B5945BD-349C-4088-9B25-D99D4FB6C01D} | 1.799926e+08 | 77255.568224 | POLYGON ((-87.58587 41.76424, -87.58597 41.764... | 2876 | 0.000016 |
| 44 | 45.0 | 95.0 | 2022-06-01 | 00:00:00.000 | 45.0 | {18480250-3E08-4D68-BF2E-96E27F6503B5} | 1.394346e+08 | 117115.300022 | POLYGON ((-87.77310 42.01148, -87.77309 42.011... | 2114 | 0.000015 |
| 20 | 21.0 | 71.0 | 2022-06-01 | 00:00:00.000 | 21.0 | {BA7C81E8-7DEB-46DF-8E71-95F9AE112FF3} | 2.118767e+08 | 92642.006031 | POLYGON ((-87.62405 41.73014, -87.62410 41.730... | 3146 | 0.000015 |
| 10 | 11.0 | 61.0 | 2022-06-01 | 00:00:00.000 | 11.0 | {C7397C87-C4AE-4C48-A00E-50D2CD1100CF} | 1.273484e+08 | 64972.869726 | POLYGON ((-87.63600 41.81635, -87.63602 41.816... | 1702 | 0.000013 |
| 12 | 13.0 | 63.0 | 2022-06-01 | 00:00:00.000 | 13.0 | {883D9536-9DDE-4FB8-B340-E9A644FD1851} | 1.480001e+08 | 59975.600111 | POLYGON ((-87.74205 41.77113, -87.74206 41.771... | 1889 | 0.000013 |
| 6 | 7.0 | 57.0 | 2022-06-01 | 00:00:00.000 | 7.0 | {279FCBD9-EA0D-4FFC-A8CA-EDA2676C0721} | 1.414924e+08 | 98906.567862 | POLYGON ((-87.54393 41.76030, -87.54370 41.760... | 1688 | 0.000012 |
| 17 | 18.0 | 68.0 | 2022-06-01 | 00:00:00.000 | 18.0 | {E22B6995-91F8-4C49-A9F3-4085D503F2A4} | 1.933789e+08 | 79337.751930 | POLYGON ((-87.65830 41.73950, -87.65834 41.739... | 2261 | 0.000012 |
| 38 | 39.0 | 89.0 | 2022-06-01 | 00:00:00.000 | 39.0 | {15480A27-2480-40CF-91EC-72B090F03522} | 1.818672e+08 | 77704.757487 | POLYGON ((-87.69916 41.98312, -87.69915 41.982... | 1934 | 0.000011 |
| 37 | 38.0 | 88.0 | 2022-06-01 | 00:00:00.000 | 38.0 | {6E5F0557-2DCA-490E-8E7D-C5B58CA65DFA} | 1.775660e+08 | 91571.573668 | POLYGON ((-87.75961 41.95327, -87.75964 41.953... | 1654 | 0.000009 |
| 8 | 9.0 | 59.0 | 2022-06-01 | 00:00:00.000 | 9.0 | {B75B90E6-CB95-4890-BBC3-D2F313E2EE80} | 2.344895e+08 | 102227.294779 | POLYGON ((-87.61414 41.72189, -87.61414 41.721... | 2129 | 0.000009 |
| 18 | 19.0 | 69.0 | 2022-06-01 | 00:00:00.000 | 19.0 | {91001AF5-B4E0-4F45-A1CB-7C22070BA22D} | 2.199899e+08 | 101405.125517 | POLYGON ((-87.66674 41.73022, -87.66677 41.730... | 1790 | 0.000008 |
| 40 | 41.0 | 91.0 | 2022-06-01 | 00:00:00.000 | 41.0 | {C885247A-E064-4FCF-A35A-747DEC142421} | 4.754967e+08 | 218605.417697 | POLYGON ((-87.79065 42.00040, -87.79058 42.000... | 1728 | 0.000004 |
| 9 | 10.0 | 60.0 | 2022-06-01 | 00:00:00.000 | 10.0 | {19FF2026-6DDD-45DA-8DF6-B44B0C15834C} | 5.773395e+08 | 147455.404991 | POLYGON ((-87.53018 41.74310, -87.53008 41.742... | 1629 | 0.000003 |
#Lets see the difference
ax=Wards.plot(column='crashes_per_area', figsize=(14,12),cmap='OrRd', legend=True)
#add more details to the map.
f, ax = plt.subplots(1, figsize=(14,12))
ax = Wards.plot(column='crashes_per_area', cmap='OrRd', legend=True, ax=ax)
lims = plt.axis('equal')
ax.set_title('Wards near Loop, Eisenhower (I-290), and I-94 Split experience highest density of vehicular crashes', weight='black', size=20)
plt.show()
#Now that we know what wards in Chicago have the most crashes, we can look into one of the tactics the city implements to improve safety and decrease crashes.
#This tactic is the implementation of speed cameras.
SpeedCam = 'Speed_Camera_Locations.csv'
if os.path.exists(SpeedCam):
print('SpeedCam:', SpeedCam)
else:
print('Please download the tutorial data or fix the path!')
SpeedCam: Speed_Camera_Locations.csv
#read file and check coordinate system
SpeedCam1 = geopandas.read_file(SpeedCam)
crs = {'init':'epsg:4326'}
#since this file was a csv, I need to convert the latitude and longitude data to be recognized geospatially. I do this with a geodataframe and the geopandas library.
#the file has a latitude and a longitude column that I can utilize to add a geometry column. I ensure that the coordinate system selected will match the rest of my data.
gspeedcam = geopandas.GeoDataFrame(
SpeedCam1, geometry=geopandas.points_from_xy(SpeedCam1.LONGITUDE, SpeedCam1.LATITUDE), crs="EPSG:4326")
gspeedcam.head()
| ADDRESS | FIRST APPROACH | SECOND APPROACH | GO-LIVE DATE | LATITUDE | LONGITUDE | LOCATION | geometry | |
|---|---|---|---|---|---|---|---|---|
| 0 | 4949 W Lawrence Ave (Speed Camera) | EB | WB | 03/22/2023 | 41.9679 | -87.7523 | (41.9679,-87.7523) | POINT (-87.75230 41.96790) |
| 1 | 7508 W Touhy Ave(Speed Camera) | EB | WB | 03/02/2023 | 42.011548085311354 | -87.81442004192668 | (42.011548085311354, -87.81442004192668) | POINT (-87.81442 42.01155) |
| 2 | 6523 N Western (Speed Camera) | NB | SB | 11/10/2013 | 42.00025951144952 | -87.68980290863908 | (42.00025951144952, -87.68980290863908) | POINT (-87.68980 42.00026) |
| 3 | 6125 N Cicero Ave (Speed Camera) | NB | SB | 03/17/2014 | 41.9921194114274 | -87.74847678515228 | (41.9921194114274, -87.74847678515228) | POINT (-87.74848 41.99212) |
| 4 | 3137 W Peterson (Speed Camera) | EB | WB | 01/03/2014 | 41.9902819135942 | -87.7095 | (41.9902819135942, -87.7095) | POINT (-87.70950 41.99028) |
#We are going to join the speed camera location data with the ward boundaries data to see what ward each camera is located in.
Speedcam_crashes_by_ward=geopandas.sjoin(gspeedcam,WardBoundaries[['ward','geometry']].copy(),predicate='within')
Speedcam_crashes_by_ward.head()
| ADDRESS | FIRST APPROACH | SECOND APPROACH | GO-LIVE DATE | LATITUDE | LONGITUDE | LOCATION | geometry | index_right | ward | |
|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 4949 W Lawrence Ave (Speed Camera) | EB | WB | 03/22/2023 | 41.9679 | -87.7523 | (41.9679,-87.7523) | POINT (-87.75230 41.96790) | 44 | 45.0 |
| 10 | 6020 W Foster Ave (Speed Camera) | WB | EB | 03/10/2023 | 41.97568071224708 | -87.7787773830256 | (41.97568071224708, -87.7787773830256) | POINT (-87.77878 41.97568) | 44 | 45.0 |
| 14 | 5471 W Higgins (Speed Camera) | EB | WB | 02/14/2014 | 41.96924714863934 | -87.76401945149458 | (41.96924714863934, -87.76401945149458) | POINT (-87.76402 41.96925) | 44 | 45.0 |
| 15 | 5432 W Lawrence (Speed Camera) | EB | WB | 02/14/2014 | 41.96785573012919 | -87.76392864369929 | (41.96785573012919, -87.76392864369929) | POINT (-87.76393 41.96786) | 44 | 45.0 |
| 1 | 7508 W Touhy Ave(Speed Camera) | EB | WB | 03/02/2023 | 42.011548085311354 | -87.81442004192668 | (42.011548085311354, -87.81442004192668) | POINT (-87.81442 42.01155) | 40 | 41.0 |
#Looks like some of the speed cameras go-live date was after 2022. We will need to get rid of those since our crash date is for 2022.
#In order to filter by date we have to convert the column from an object to a date field.
Speedcam_crashes_by_ward.dtypes
ADDRESS object
FIRST APPROACH object
SECOND APPROACH object
GO-LIVE DATE object
LATITUDE object
LONGITUDE object
LOCATION object
geometry geometry
index_right int64
ward float64
dtype: object
Speedcam_crashes_by_ward["GO-LIVE DATE"]=pd.to_datetime(Speedcam_crashes_by_ward["GO-LIVE DATE"])
Speedcam_crashes_by_ward.head()
| ADDRESS | FIRST APPROACH | SECOND APPROACH | GO-LIVE DATE | LATITUDE | LONGITUDE | LOCATION | geometry | index_right | ward | |
|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 4949 W Lawrence Ave (Speed Camera) | EB | WB | 2023-03-22 | 41.9679 | -87.7523 | (41.9679,-87.7523) | POINT (-87.75230 41.96790) | 44 | 45.0 |
| 10 | 6020 W Foster Ave (Speed Camera) | WB | EB | 2023-03-10 | 41.97568071224708 | -87.7787773830256 | (41.97568071224708, -87.7787773830256) | POINT (-87.77878 41.97568) | 44 | 45.0 |
| 14 | 5471 W Higgins (Speed Camera) | EB | WB | 2014-02-14 | 41.96924714863934 | -87.76401945149458 | (41.96924714863934, -87.76401945149458) | POINT (-87.76402 41.96925) | 44 | 45.0 |
| 15 | 5432 W Lawrence (Speed Camera) | EB | WB | 2014-02-14 | 41.96785573012919 | -87.76392864369929 | (41.96785573012919, -87.76392864369929) | POINT (-87.76393 41.96786) | 44 | 45.0 |
| 1 | 7508 W Touhy Ave(Speed Camera) | EB | WB | 2023-03-02 | 42.011548085311354 | -87.81442004192668 | (42.011548085311354, -87.81442004192668) | POINT (-87.81442 42.01155) | 40 | 41.0 |
Speedcam_crashes_by_ward.dtypes
ADDRESS object
FIRST APPROACH object
SECOND APPROACH object
GO-LIVE DATE datetime64[ns]
LATITUDE object
LONGITUDE object
LOCATION object
geometry geometry
index_right int64
ward float64
dtype: object
Speedcam_crashes_by_ward.sort_values(by='GO-LIVE DATE', ascending=False)
| ADDRESS | FIRST APPROACH | SECOND APPROACH | GO-LIVE DATE | LATITUDE | LONGITUDE | LOCATION | geometry | index_right | ward | |
|---|---|---|---|---|---|---|---|---|---|---|
| 142 | 4350 W 79th St (Speed Camera) | WB | 2023-05-19 | 41.749426144675795 | -87.72979639455757 | (41.749426144675795, -87.72979639455757) | POINT (-87.72980 41.74943) | 17 | 18.0 | |
| 96 | 3314 W 16th St (Speed Camera) | EB | WB | 2023-05-08 | 41.8591 | -87.70866665927319 | (41.8591, -87.70866665927319) | POINT (-87.70867 41.85910) | 23 | 24.0 |
| 100 | 3536 S Wallace St (Speed Camea) | NB | SB | 2023-05-08 | 41.8297474389017 | -87.641307410277 | (41.8297474389017, -87.641307410277) | POINT (-87.64131 41.82975) | 10 | 11.0 |
| 144 | 1215 E 83RD ST (Speed Camera) | EB | WB | 2023-05-05 | 41.7441 | -87.5939 | (41.7441, -87.5939) | POINT (-87.59390 41.74410) | 7 | 8.0 |
| 57 | 2223 N Kedzie Blvd (Speed Camera) | NB | SB | 2023-05-02 | 41.9219894043721 | -87.7070308742212 | (41.9219894043721, -87.7070308742212) | POINT (-87.70703 41.92199) | 0 | 1.0 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 130 | 3450 W 71ST ST (Speed Camera) | EB | WB | 2013-09-06 | 41.76439095689881 | -87.70972841983624 | (41.76439095689881, -87.70972841983624) | POINT (-87.70973 41.76439) | 16 | 17.0 |
| 126 | 6909 S Kedzie Ave (Speed Camera) | NB | 2013-09-06 | 41.76773186901884 | -87.70273790708362 | (41.76773186901884, -87.70273790708362) | POINT (-87.70274 41.76773) | 16 | 17.0 | |
| 125 | 6818 S Kedzie Ave (Speed Camera) | SB | 2013-09-06 | 41.769106296454154 | -87.70305895795853 | (41.769106296454154, -87.70305895795853) | POINT (-87.70306 41.76911) | 16 | 17.0 | |
| 11 | 4124 W Foster Ave (Speed Camera) | EB | WB | 2013-08-26 | 41.975501 | -87.73166979484374 | (41.975501, -87.73166979484374) | POINT (-87.73167 41.97550) | 38 | 39.0 |
| 12 | 5120 N Pulaski Rd (Speed Camera) | NB | SB | 2013-08-26 | 41.97433265461055 | -87.728215 | (41.97433265461055, -87.728215) | POINT (-87.72822 41.97433) | 38 | 39.0 |
161 rows × 10 columns
#We want to get rid of any cameras that were placed in 2022 or after because the crash data is only for 2022 and we want to look at the effects of the cameras that were there for the entire year.
Speedcam_crashes_by_ward=Speedcam_crashes_by_ward[(Speedcam_crashes_by_ward['GO-LIVE DATE'] < '2022-01-01')]
Speedcam_crashes_by_ward.sort_values(by='GO-LIVE DATE', ascending=False)
| ADDRESS | FIRST APPROACH | SECOND APPROACH | GO-LIVE DATE | LATITUDE | LONGITUDE | LOCATION | geometry | index_right | ward | |
|---|---|---|---|---|---|---|---|---|---|---|
| 135 | 1330 W 76th St(Speed Camera) | EB | WB | 2021-07-09 | 41.75604940593661 | -87.65705959049882 | (41.75604940593661, -87.65705959049882) | POINT (-87.65706 41.75605) | 16 | 17.0 |
| 67 | 1444 W Division St(Speed Camera) | WB | 2018-09-05 | 41.903482 | -87.664378 | (41.903482, -87.664378) | POINT (-87.66438 41.90348) | 0 | 1.0 | |
| 68 | 1455 W Division St(Speed Camera) | EB | 2018-09-05 | 41.90329 | -87.664873 | (41.90329, -87.664873) | POINT (-87.66487 41.90329) | 26 | 27.0 | |
| 102 | 4246 W 47th St(Speed Camera) | WB | 2018-08-27 | 41.807853 | -87.730128 | (41.807853, -87.730128) | POINT (-87.73013 41.80785) | 13 | 14.0 | |
| 103 | 4319 W 47th St(Speed Camera) | EB | 2018-08-27 | 41.807645 | -87.731753 | (41.807645, -87.731753) | POINT (-87.73175 41.80765) | 13 | 14.0 | |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 125 | 6818 S Kedzie Ave (Speed Camera) | SB | 2013-09-06 | 41.769106296454154 | -87.70305895795853 | (41.769106296454154, -87.70305895795853) | POINT (-87.70306 41.76911) | 16 | 17.0 | |
| 130 | 3450 W 71ST ST (Speed Camera) | EB | WB | 2013-09-06 | 41.76439095689881 | -87.70972841983624 | (41.76439095689881, -87.70972841983624) | POINT (-87.70973 41.76439) | 16 | 17.0 |
| 126 | 6909 S Kedzie Ave (Speed Camera) | NB | 2013-09-06 | 41.76773186901884 | -87.70273790708362 | (41.76773186901884, -87.70273790708362) | POINT (-87.70274 41.76773) | 16 | 17.0 | |
| 11 | 4124 W Foster Ave (Speed Camera) | EB | WB | 2013-08-26 | 41.975501 | -87.73166979484374 | (41.975501, -87.73166979484374) | POINT (-87.73167 41.97550) | 38 | 39.0 |
| 12 | 5120 N Pulaski Rd (Speed Camera) | NB | SB | 2013-08-26 | 41.97433265461055 | -87.728215 | (41.97433265461055, -87.728215) | POINT (-87.72822 41.97433) | 38 | 39.0 |
146 rows × 10 columns
#This removed 15 rows! These speed cameras were likely in different locations before 2022 and could have an effect on the results.
#Map the speed cameras and utilize the crash choropleth map as a basemap. This will help me see if speed cameras are located in areas with more crashes.
base=Wards.plot(figsize=(14,12), column='crashes_per_area', cmap='OrRd', linewidth=2, legend=True)
Speedcam_crashes_by_ward.plot(ax=base,figsize=(14,12),marker='P', color='blue', markersize=20, edgecolor='ghostwhite')
<AxesSubplot:>
#We are now going to summarize this data to see how many speed cameras are in each ward and sort descending by number of cameras.
Wards.ward = Wards.ward.astype('float64').astype(str)
speedcam_counts_per_ward=Speedcam_crashes_by_ward.groupby('ward',as_index=False).size()
speedcam_counts_per_ward.rename(columns={"size":"cameras"},inplace=True)
speedcam_counts_per_ward.ward = speedcam_counts_per_ward.ward.astype('float64').astype(str)
speedcam_counts_per_ward.sort_values(by='cameras')
| ward | cameras | |
|---|---|---|
| 19 | 25.0 | 1 |
| 34 | 41.0 | 1 |
| 33 | 40.0 | 1 |
| 16 | 21.0 | 1 |
| 11 | 16.0 | 1 |
| 8 | 12.0 | 1 |
| 7 | 11.0 | 1 |
| 39 | 48.0 | 1 |
| 5 | 9.0 | 2 |
| 36 | 45.0 | 2 |
| 4 | 8.0 | 2 |
| 1 | 4.0 | 2 |
| 28 | 35.0 | 2 |
| 27 | 33.0 | 2 |
| 17 | 23.0 | 2 |
| 21 | 27.0 | 2 |
| 37 | 46.0 | 3 |
| 2 | 5.0 | 3 |
| 30 | 37.0 | 3 |
| 25 | 31.0 | 3 |
| 23 | 29.0 | 3 |
| 0 | 1.0 | 3 |
| 3 | 6.0 | 3 |
| 18 | 24.0 | 4 |
| 14 | 19.0 | 4 |
| 6 | 10.0 | 4 |
| 15 | 20.0 | 5 |
| 29 | 36.0 | 5 |
| 12 | 17.0 | 5 |
| 35 | 42.0 | 5 |
| 10 | 15.0 | 5 |
| 24 | 30.0 | 5 |
| 38 | 47.0 | 5 |
| 22 | 28.0 | 6 |
| 20 | 26.0 | 6 |
| 31 | 38.0 | 7 |
| 26 | 32.0 | 8 |
| 32 | 39.0 | 8 |
| 9 | 14.0 | 9 |
| 13 | 18.0 | 10 |
#join this summarized data with the Wards data so we can compare side by side. Replace NAN with zero (0) for wards without any cameras.
Wards=Wards.merge(speedcam_counts_per_ward, on='ward', how='left')
Wards=Wards.fillna(0)
Wards.head()
| ward | objectid | date_edit_ | time_edit_ | ward_id | globalid | st_area_sh | st_length_ | geometry | number_of_crashes | crashes_per_area | cameras | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1.0 | 51.0 | 2022-06-01 | 00:00:00.000 | 1.0 | {DB2A2A7D-FAF1-42A4-B061-AE18C31A80BB} | 6.589346e+07 | 61878.821587 | POLYGON ((-87.68777 41.92858, -87.68776 41.928... | 1815 | 0.000028 | 3.0 |
| 1 | 2.0 | 52.0 | 2022-06-01 | 00:00:00.000 | 2.0 | {88F300F6-D6DF-4337-8DE3-0C2D27A5B338} | 3.128511e+07 | 74175.949239 | POLYGON ((-87.62517 41.90400, -87.62500 41.904... | 2023 | 0.000065 | 0.0 |
| 2 | 3.0 | 53.0 | 2022-06-01 | 00:00:00.000 | 3.0 | {4F169974-0F93-4C35-98C1-20A8415C72F5} | 9.777341e+07 | 85625.597620 | POLYGON ((-87.61992 41.86577, -87.61985 41.865... | 2058 | 0.000021 | 0.0 |
| 3 | 4.0 | 54.0 | 2022-06-01 | 00:00:00.000 | 4.0 | {02DC7BBB-BD1E-413C-994E-0A3154FD3D54} | 1.158091e+08 | 103594.867171 | POLYGON ((-87.62429 41.87700, -87.62429 41.876... | 2488 | 0.000021 | 2.0 |
| 4 | 5.0 | 55.0 | 2022-06-01 | 00:00:00.000 | 5.0 | {0A109A41-9DED-47D7-934E-1EA1CC7EE025} | 1.120803e+08 | 88207.690241 | POLYGON ((-87.56030 41.76636, -87.56058 41.766... | 2101 | 0.000019 | 3.0 |
speedcammean=Wards["cameras"].mean()
speedcammedian =Wards["cameras"].median()
print("The average ward has %s speed cameras. The median number of speed cameras is %s" % (speedcammean, speedcammedian))
The average ward has 2.92 speed cameras. The median number of speed cameras is 2.0
Wards.sort_values(by='cameras', ascending=False)
| ward | objectid | date_edit_ | time_edit_ | ward_id | globalid | st_area_sh | st_length_ | geometry | number_of_crashes | crashes_per_area | cameras | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 17 | 18.0 | 68.0 | 2022-06-01 | 00:00:00.000 | 18.0 | {E22B6995-91F8-4C49-A9F3-4085D503F2A4} | 1.933789e+08 | 79337.751930 | POLYGON ((-87.65830 41.73950, -87.65834 41.739... | 2261 | 0.000012 | 10.0 |
| 13 | 14.0 | 64.0 | 2022-06-01 | 00:00:00.000 | 14.0 | {612295DC-E128-468C-B0C9-A1ADE1CB1C84} | 1.213109e+08 | 72296.250663 | POLYGON ((-87.69421 41.80828, -87.69420 41.808... | 2322 | 0.000019 | 9.0 |
| 31 | 32.0 | 82.0 | 2022-06-01 | 00:00:00.000 | 32.0 | {2106D44D-07AC-4B39-A878-BEBB439D1BF3} | 9.288828e+07 | 47973.924224 | POLYGON ((-87.68097 41.94636, -87.68097 41.946... | 2228 | 0.000024 | 8.0 |
| 38 | 39.0 | 89.0 | 2022-06-01 | 00:00:00.000 | 39.0 | {15480A27-2480-40CF-91EC-72B090F03522} | 1.818672e+08 | 77704.757487 | POLYGON ((-87.69916 41.98312, -87.69915 41.982... | 1934 | 0.000011 | 8.0 |
| 37 | 38.0 | 88.0 | 2022-06-01 | 00:00:00.000 | 38.0 | {6E5F0557-2DCA-490E-8E7D-C5B58CA65DFA} | 1.775660e+08 | 91571.573668 | POLYGON ((-87.75961 41.95327, -87.75964 41.953... | 1654 | 0.000009 | 7.0 |
| 25 | 26.0 | 76.0 | 2022-06-01 | 00:00:00.000 | 26.0 | {BB18C0C9-4A3A-43F8-A953-6A8B4D2487D5} | 7.767961e+07 | 60930.848090 | POLYGON ((-87.73649 41.92253, -87.73648 41.922... | 1568 | 0.000020 | 6.0 |
| 27 | 28.0 | 78.0 | 2022-06-01 | 00:00:00.000 | 28.0 | {65BDECD2-AB03-4F1B-8025-49E3B1029A49} | 1.600439e+08 | 129592.820270 | POLYGON ((-87.72098 41.88802, -87.71974 41.888... | 4611 | 0.000029 | 6.0 |
| 41 | 42.0 | 92.0 | 2022-06-01 | 00:00:00.000 | 42.0 | {AE139837-FCC1-4E2A-84A3-15B2B67D65A4} | 5.253453e+07 | 68484.113474 | POLYGON ((-87.62685 41.89668, -87.62685 41.896... | 3886 | 0.000074 | 5.0 |
| 35 | 36.0 | 86.0 | 2022-06-01 | 00:00:00.000 | 36.0 | {337BAF17-7684-4F7F-BFCA-0BFD2B92B869} | 8.383845e+07 | 118667.485047 | POLYGON ((-87.76599 41.92398, -87.76591 41.923... | 2126 | 0.000025 | 5.0 |
| 29 | 30.0 | 80.0 | 2022-06-01 | 00:00:00.000 | 30.0 | {15C33223-51B6-456F-B790-BEC795885C33} | 9.490456e+07 | 81414.586459 | POLYGON ((-87.73242 41.95181, -87.73242 41.951... | 1768 | 0.000019 | 5.0 |
| 14 | 15.0 | 65.0 | 2022-06-01 | 00:00:00.000 | 15.0 | {51F05987-1A65-4707-B17E-98C8709061B1} | 8.307862e+07 | 107657.780595 | POLYGON ((-87.63584 41.80905, -87.63585 41.809... | 1762 | 0.000021 | 5.0 |
| 16 | 17.0 | 67.0 | 2022-06-01 | 00:00:00.000 | 17.0 | {A0475DAB-473E-447B-9F82-AEC0CA483C93} | 1.301704e+08 | 90154.781522 | POLYGON ((-87.65421 41.76869, -87.65421 41.768... | 2461 | 0.000019 | 5.0 |
| 46 | 47.0 | 97.0 | 2022-06-01 | 00:00:00.000 | 47.0 | {4F17B913-08A1-4EF6-BF20-0EF4B6478494} | 8.481029e+07 | 55157.008247 | POLYGON ((-87.65979 41.97332, -87.65978 41.973... | 1554 | 0.000018 | 5.0 |
| 19 | 20.0 | 70.0 | 2022-06-01 | 00:00:00.000 | 20.0 | {1EA93731-81AC-42D6-9711-283C3A3CA2BE} | 1.453499e+08 | 100221.251179 | POLYGON ((-87.62082 41.79383, -87.62050 41.793... | 2505 | 0.000017 | 5.0 |
| 9 | 10.0 | 60.0 | 2022-06-01 | 00:00:00.000 | 10.0 | {19FF2026-6DDD-45DA-8DF6-B44B0C15834C} | 5.773395e+08 | 147455.404991 | POLYGON ((-87.53018 41.74310, -87.53008 41.742... | 1629 | 0.000003 | 4.0 |
| 23 | 24.0 | 74.0 | 2022-06-01 | 00:00:00.000 | 24.0 | {9ED052F7-F703-4A2D-A829-363FB60472C4} | 1.120000e+08 | 68816.597232 | POLYGON ((-87.69070 41.85635, -87.69077 41.856... | 2664 | 0.000024 | 4.0 |
| 18 | 19.0 | 69.0 | 2022-06-01 | 00:00:00.000 | 19.0 | {91001AF5-B4E0-4F45-A1CB-7C22070BA22D} | 2.199899e+08 | 101405.125517 | POLYGON ((-87.66674 41.73022, -87.66677 41.730... | 1790 | 0.000008 | 4.0 |
| 36 | 37.0 | 87.0 | 2022-06-01 | 00:00:00.000 | 37.0 | {35FCA85E-7EA3-4399-9077-7EACCF85268E} | 1.004835e+08 | 56306.600969 | POLYGON ((-87.72266 41.90316, -87.72266 41.903... | 2650 | 0.000026 | 3.0 |
| 30 | 31.0 | 81.0 | 2022-06-01 | 00:00:00.000 | 31.0 | {0E9C61BE-C5B1-4051-AB5C-833CB7887482} | 7.442608e+07 | 52347.368005 | POLYGON ((-87.72392 41.93913, -87.72371 41.939... | 1934 | 0.000026 | 3.0 |
| 45 | 46.0 | 96.0 | 2022-06-01 | 00:00:00.000 | 46.0 | {C894F602-BEBF-4984-803F-736F2BB8F6DE} | 5.592727e+07 | 52699.506856 | POLYGON ((-87.64521 41.96974, -87.64516 41.969... | 1349 | 0.000024 | 3.0 |
| 28 | 29.0 | 79.0 | 2022-06-01 | 00:00:00.000 | 29.0 | {C1A9937E-2864-4CFB-ADD5-B8397C56DC77} | 1.193660e+08 | 97816.079177 | POLYGON ((-87.80127 41.93944, -87.80127 41.939... | 2413 | 0.000020 | 3.0 |
| 0 | 1.0 | 51.0 | 2022-06-01 | 00:00:00.000 | 1.0 | {DB2A2A7D-FAF1-42A4-B061-AE18C31A80BB} | 6.589346e+07 | 61878.821587 | POLYGON ((-87.68777 41.92858, -87.68776 41.928... | 1815 | 0.000028 | 3.0 |
| 4 | 5.0 | 55.0 | 2022-06-01 | 00:00:00.000 | 5.0 | {0A109A41-9DED-47D7-934E-1EA1CC7EE025} | 1.120803e+08 | 88207.690241 | POLYGON ((-87.56030 41.76636, -87.56058 41.766... | 2101 | 0.000019 | 3.0 |
| 5 | 6.0 | 56.0 | 2022-06-01 | 00:00:00.000 | 6.0 | {FD74A999-4BBA-4CE3-BEBB-CC76423037E8} | 1.392022e+08 | 80779.851890 | POLYGON ((-87.61794 41.77292, -87.61786 41.772... | 3181 | 0.000023 | 3.0 |
| 22 | 23.0 | 73.0 | 2022-06-01 | 00:00:00.000 | 23.0 | {51BB999E-306A-4091-8F7E-4241AACA3340} | 1.168305e+08 | 94602.226753 | POLYGON ((-87.71024 41.77156, -87.71039 41.771... | 2082 | 0.000018 | 2.0 |
| 32 | 33.0 | 83.0 | 2022-06-01 | 00:00:00.000 | 33.0 | {F01E135A-54DB-49B2-867E-7F2DCD35A1F6} | 6.217322e+07 | 48801.435311 | POLYGON ((-87.70353 41.97186, -87.70344 41.971... | 1395 | 0.000022 | 2.0 |
| 3 | 4.0 | 54.0 | 2022-06-01 | 00:00:00.000 | 4.0 | {02DC7BBB-BD1E-413C-994E-0A3154FD3D54} | 1.158091e+08 | 103594.867171 | POLYGON ((-87.62429 41.87700, -87.62429 41.876... | 2488 | 0.000021 | 2.0 |
| 44 | 45.0 | 95.0 | 2022-06-01 | 00:00:00.000 | 45.0 | {18480250-3E08-4D68-BF2E-96E27F6503B5} | 1.394346e+08 | 117115.300022 | POLYGON ((-87.77310 42.01148, -87.77309 42.011... | 2114 | 0.000015 | 2.0 |
| 8 | 9.0 | 59.0 | 2022-06-01 | 00:00:00.000 | 9.0 | {B75B90E6-CB95-4890-BBC3-D2F313E2EE80} | 2.344895e+08 | 102227.294779 | POLYGON ((-87.61414 41.72189, -87.61414 41.721... | 2129 | 0.000009 | 2.0 |
| 34 | 35.0 | 85.0 | 2022-06-01 | 00:00:00.000 | 35.0 | {3FDB81BF-D813-4AA7-9F92-DD74FD23B646} | 7.613137e+07 | 70987.979595 | POLYGON ((-87.69620 41.94670, -87.69620 41.946... | 1772 | 0.000023 | 2.0 |
| 7 | 8.0 | 58.0 | 2022-06-01 | 00:00:00.000 | 8.0 | {2B5945BD-349C-4088-9B25-D99D4FB6C01D} | 1.799926e+08 | 77255.568224 | POLYGON ((-87.58587 41.76424, -87.58597 41.764... | 2876 | 0.000016 | 2.0 |
| 26 | 27.0 | 77.0 | 2022-06-01 | 00:00:00.000 | 27.0 | {5EFBE372-72FB-4702-8C3C-BA882825EEF1} | 1.595750e+08 | 129446.885360 | POLYGON ((-87.63420 41.89660, -87.63426 41.896... | 4334 | 0.000027 | 2.0 |
| 10 | 11.0 | 61.0 | 2022-06-01 | 00:00:00.000 | 11.0 | {C7397C87-C4AE-4C48-A00E-50D2CD1100CF} | 1.273484e+08 | 64972.869726 | POLYGON ((-87.63600 41.81635, -87.63602 41.816... | 1702 | 0.000013 | 1.0 |
| 20 | 21.0 | 71.0 | 2022-06-01 | 00:00:00.000 | 21.0 | {BA7C81E8-7DEB-46DF-8E71-95F9AE112FF3} | 2.118767e+08 | 92642.006031 | POLYGON ((-87.62405 41.73014, -87.62410 41.730... | 3146 | 0.000015 | 1.0 |
| 47 | 48.0 | 98.0 | 2022-06-01 | 00:00:00.000 | 48.0 | {174DEC8E-5281-4774-831E-A7E0F6F3440D} | 4.500890e+07 | 35618.125393 | POLYGON ((-87.64430 41.96990, -87.64516 41.969... | 1380 | 0.000031 | 1.0 |
| 24 | 25.0 | 75.0 | 2022-06-01 | 00:00:00.000 | 25.0 | {B1A8F67E-3BEB-4E6A-967B-DB2271B162C5} | 9.329780e+07 | 80678.887175 | POLYGON ((-87.63901 41.86002, -87.63901 41.859... | 2113 | 0.000023 | 1.0 |
| 11 | 12.0 | 62.0 | 2022-06-01 | 00:00:00.000 | 12.0 | {EAD019CB-AA66-48C4-9338-795B4E083B59} | 1.334616e+08 | 76139.484398 | POLYGON ((-87.66588 41.84493, -87.66581 41.844... | 2300 | 0.000017 | 1.0 |
| 39 | 40.0 | 90.0 | 2022-06-01 | 00:00:00.000 | 40.0 | {EA278E33-45CB-4D28-BEE1-F64C155E65B9} | 9.664484e+07 | 67854.255079 | POLYGON ((-87.68152 42.00812, -87.68144 42.007... | 1604 | 0.000017 | 1.0 |
| 40 | 41.0 | 91.0 | 2022-06-01 | 00:00:00.000 | 41.0 | {C885247A-E064-4FCF-A35A-747DEC142421} | 4.754967e+08 | 218605.417697 | POLYGON ((-87.79065 42.00040, -87.79058 42.000... | 1728 | 0.000004 | 1.0 |
| 15 | 16.0 | 66.0 | 2022-06-01 | 00:00:00.000 | 16.0 | {B54690B6-21DF-44F5-B898-D73EF9976BDC} | 1.258524e+08 | 90382.506038 | POLYGON ((-87.64077 41.77616, -87.64082 41.776... | 2490 | 0.000020 | 1.0 |
| 6 | 7.0 | 57.0 | 2022-06-01 | 00:00:00.000 | 7.0 | {279FCBD9-EA0D-4FFC-A8CA-EDA2676C0721} | 1.414924e+08 | 98906.567862 | POLYGON ((-87.54393 41.76030, -87.54370 41.760... | 1688 | 0.000012 | 0.0 |
| 21 | 22.0 | 72.0 | 2022-06-01 | 00:00:00.000 | 22.0 | {0705D4DE-80BF-473A-B3B7-078D63AD0298} | 1.055096e+08 | 60413.987154 | POLYGON ((-87.70761 41.84995, -87.70761 41.849... | 1843 | 0.000017 | 0.0 |
| 42 | 43.0 | 93.0 | 2022-06-01 | 00:00:00.000 | 43.0 | {1003F8BC-CAE5-46E7-8A50-95CC5B60E8C7} | 6.223707e+07 | 48459.476947 | POLYGON ((-87.63183 41.93305, -87.63183 41.932... | 1453 | 0.000023 | 0.0 |
| 43 | 44.0 | 94.0 | 2022-06-01 | 00:00:00.000 | 44.0 | {E181C590-6E28-4CF9-85A1-96AF19075184} | 4.750785e+07 | 36707.423435 | POLYGON ((-87.65682 41.95140, -87.65681 41.951... | 1402 | 0.000030 | 0.0 |
| 1 | 2.0 | 52.0 | 2022-06-01 | 00:00:00.000 | 2.0 | {88F300F6-D6DF-4337-8DE3-0C2D27A5B338} | 3.128511e+07 | 74175.949239 | POLYGON ((-87.62517 41.90400, -87.62500 41.904... | 2023 | 0.000065 | 0.0 |
| 12 | 13.0 | 63.0 | 2022-06-01 | 00:00:00.000 | 13.0 | {883D9536-9DDE-4FB8-B340-E9A644FD1851} | 1.480001e+08 | 59975.600111 | POLYGON ((-87.74205 41.77113, -87.74206 41.771... | 1889 | 0.000013 | 0.0 |
| 2 | 3.0 | 53.0 | 2022-06-01 | 00:00:00.000 | 3.0 | {4F169974-0F93-4C35-98C1-20A8415C72F5} | 9.777341e+07 | 85625.597620 | POLYGON ((-87.61992 41.86577, -87.61985 41.865... | 2058 | 0.000021 | 0.0 |
| 33 | 34.0 | 84.0 | 2022-06-01 | 00:00:00.000 | 34.0 | {54EBC7A4-FE32-4936-A72D-33A39D37248F} | 4.272862e+07 | 75371.506020 | POLYGON ((-87.63968 41.88906, -87.63924 41.887... | 2200 | 0.000051 | 0.0 |
| 48 | 49.0 | 99.0 | 2022-06-01 | 00:00:00.000 | 49.0 | {F7B2FFFC-D9B6-463F-BFF0-8C5C2ED72D47} | 5.088363e+07 | 37398.359952 | POLYGON ((-87.65729 42.00418, -87.65729 42.004... | 1293 | 0.000025 | 0.0 |
| 49 | 50.0 | 100.0 | 2022-06-01 | 00:00:00.000 | 50.0 | {C4F10402-10E2-4401-BD4D-7FBDAF35E3FC} | 7.450479e+07 | 60789.219509 | POLYGON ((-87.68189 42.00896, -87.68204 42.008... | 1685 | 0.000023 | 0.0 |
#Graphing the data to see the numbers side by side for each ward.
import numpy as np
import matplotlib.patches as mpatches
WardsChart=Wards.sort_values(['crashes_per_area'], ascending=False)
ax1=WardsChart.plot(x="ward", y="cameras", kind="bar",color='grey',position=0, width=0.3)
ax2 = WardsChart['crashes_per_area'].plot(secondary_y=True, color='navy',figsize=(50,20),kind='bar',position=1,width=0.3)
ax1.set_xlabel('Wards', fontdict={'fontsize': 40, 'fontweight': 'bold'})
ax1.set_ylabel('Cameras',fontdict={'fontsize': 40, 'fontweight': 'bold'})
ax2.set_ylabel('Crash Density',fontdict={'fontsize': 40, 'fontweight': 'bold'})
ax1.tick_params(axis='both',labelsize=30)
ax1.set_xticklabels(ax1.get_xticks(), rotation = 0)
ax2.tick_params(axis='both',labelsize=30)
ax1.legend(['Speed Cameras'],fontsize=30, loc='best', bbox_to_anchor=(0.15, 1))
plt.legend(['Density of Crashes'],fontsize=30, bbox_to_anchor=(0.0156, .94))
plt.show()
Most wards have at least one (1) speed camera, and ten (10) do not (not shown in the list above due to how I joined the data). These ten (10) most likely did have speed cameras at some point during 2022, but moved locations since. Ward 18 is the sixth largest (geographically) ward, so it is not extremely surprising to see it having more speed cameras, but, it has the most at ten (10). Of the top ten (10) wards with the most cameras, none are in the top ten (10) geographically largest wards. Of the bottom ten (10) wards for cameras, two (2) are in the bottom ten (10) geograaphical sized wards. Looking at crashes, one ward in the top five (5) for speed cameras is in the bottom ten (10) for crashes, and no wards with zero (0) cameras are in the top ten (10) for crashes. It is important to note that speed cameras are not on highways so wards where the majority of crashes occur on highways are not helped by the current placement of speed cameras.
Looking at crash density, three (3) of the top ten (10) wards for highest density of crashes have zero (0) speed cameras. Only two (2) wards in the bottom ten (10) for crash density are also in the top ten (10) for speed cameras. It is important to think about the difference in number of cameras. The lowest amount is zero (0) and the highest is ten (10). This is a big range. Overall, this analysis is inconclusive on its own and will need further investigation.
#Analyzing the relationship between the two variables
x=Wards.cameras
y=Wards.crashes_per_area
plt.scatter(x,y)
z=np.polyfit(x, y, 1)
p = np.poly1d(z)
plt.plot(x,p(x),"r")
plt.xlabel("Speed Cameras")
plt.ylabel("Density of Crashes")
plt.show()
#The chart above shows a very small relationship. Finding the correlation between the variables will help me understand how strong this is.
#Finding the covariance
covariance = np.cov(x, y)
print(covariance)
#Finding Pearson's correlation p-value
from scipy.stats import pearsonr
corr, _ = pearsonr(x,y)
print('Pearsons correlation: %.3f' % corr)
#Finding Spearmans correlation p-value, which is helpful for understanding nonlinear relationships
from scipy.stats import spearmanr
corr, _ = spearmanr(x, y)
print('Spearmans correlation: %.3f' % corr)
[[ 6.72816327e+00 -4.23426976e-06]
[-4.23426976e-06 1.57822963e-10]]
Pearsons correlation: -0.130
Spearmans correlation: -0.104
#There is a very small, negative correlation between the variables. This is negligible correlation. Thus, the relationship of this data is statistically insignificant.
#let's see if this changes when we only look at speed related crashes.
print(TrafficCrashes.columns)
Index(['crash_reco', 'date_crash', 'time_crash', 'posted_spe', 'first_cras',
'trafficway', 'crash_type', 'hit_and_ru', 'damage', 'prim_contr',
'sec_contri', 'street_no', 'street_dir', 'street_nam', 'dooring_i',
'num_units', 'most_sever', 'injuries_t', 'injuries_f', 'injuries_i',
'injuries_n', 'injuries_r', 'injuries_2', 'injuries_u', 'latitude',
'longitude', 'geometry'],
dtype='object')
SpeedCrashes = TrafficCrashes[(TrafficCrashes['prim_contr']=='EXCEEDING SAFE SPEED FOR CONDITIONS') | (TrafficCrashes['prim_contr']=='FAILING TO REDUCE SPEED TO AVOID CRASH')]
speedcrashes_by_ward=geopandas.sjoin(SpeedCrashes,WardBoundaries[['ward','geometry']].copy(),predicate='within')
speedcounts_per_ward=speedcrashes_by_ward.groupby('ward',as_index=False).size()
speedcounts_per_ward.rename(columns={"size":"number_of_speedcrashes"},inplace=True)
speedcounts_per_ward.sort_values(by='number_of_speedcrashes',axis=0, ascending=False)
speedWards = WardBoundaries.merge(speedcounts_per_ward, on='ward')
speedWards["speedcrashes_per_area"]=speedWards["number_of_speedcrashes"] / speedWards["st_area_sh"]
speedWards.ward = speedWards.ward.astype('float64').astype(str)
speedWards=speedWards.merge(speedcam_counts_per_ward, on='ward',how='left')
speedWards=speedWards.fillna(0)
base=speedWards.plot(figsize=(14,12), column='speedcrashes_per_area', cmap='OrRd', linewidth=2, legend=True)
Speedcam_crashes_by_ward.plot(ax=base,figsize=(14,12),marker='P', color='blue', markersize=20, edgecolor='ghostwhite')
<AxesSubplot:>
speedWards.sort_values(by='speedcrashes_per_area', ascending=False)
| ward | objectid | date_edit_ | time_edit_ | ward_id | globalid | st_area_sh | st_length_ | geometry | number_of_speedcrashes | speedcrashes_per_area | cameras | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 2.0 | 52.0 | 2022-06-01 | 00:00:00.000 | 2.0 | {88F300F6-D6DF-4337-8DE3-0C2D27A5B338} | 3.128511e+07 | 74175.949239 | POLYGON ((-87.62517 41.90400, -87.62500 41.904... | 101 | 3.228373e-06 | 0.0 |
| 41 | 42.0 | 92.0 | 2022-06-01 | 00:00:00.000 | 42.0 | {AE139837-FCC1-4E2A-84A3-15B2B67D65A4} | 5.253453e+07 | 68484.113474 | POLYGON ((-87.62685 41.89668, -87.62685 41.896... | 163 | 3.102721e-06 | 5.0 |
| 33 | 34.0 | 84.0 | 2022-06-01 | 00:00:00.000 | 34.0 | {54EBC7A4-FE32-4936-A72D-33A39D37248F} | 4.272862e+07 | 75371.506020 | POLYGON ((-87.63968 41.88906, -87.63924 41.887... | 71 | 1.661650e-06 | 0.0 |
| 0 | 1.0 | 51.0 | 2022-06-01 | 00:00:00.000 | 1.0 | {DB2A2A7D-FAF1-42A4-B061-AE18C31A80BB} | 6.589346e+07 | 61878.821587 | POLYGON ((-87.68777 41.92858, -87.68776 41.928... | 83 | 1.259609e-06 | 3.0 |
| 36 | 37.0 | 87.0 | 2022-06-01 | 00:00:00.000 | 37.0 | {35FCA85E-7EA3-4399-9077-7EACCF85268E} | 1.004835e+08 | 56306.600969 | POLYGON ((-87.72266 41.90316, -87.72266 41.903... | 126 | 1.253937e-06 | 3.0 |
| 30 | 31.0 | 81.0 | 2022-06-01 | 00:00:00.000 | 31.0 | {0E9C61BE-C5B1-4051-AB5C-833CB7887482} | 7.442608e+07 | 52347.368005 | POLYGON ((-87.72392 41.93913, -87.72371 41.939... | 84 | 1.128637e-06 | 3.0 |
| 34 | 35.0 | 85.0 | 2022-06-01 | 00:00:00.000 | 35.0 | {3FDB81BF-D813-4AA7-9F92-DD74FD23B646} | 7.613137e+07 | 70987.979595 | POLYGON ((-87.69620 41.94670, -87.69620 41.946... | 85 | 1.116491e-06 | 2.0 |
| 3 | 4.0 | 54.0 | 2022-06-01 | 00:00:00.000 | 4.0 | {02DC7BBB-BD1E-413C-994E-0A3154FD3D54} | 1.158091e+08 | 103594.867171 | POLYGON ((-87.62429 41.87700, -87.62429 41.876... | 127 | 1.096632e-06 | 2.0 |
| 14 | 15.0 | 65.0 | 2022-06-01 | 00:00:00.000 | 15.0 | {51F05987-1A65-4707-B17E-98C8709061B1} | 8.307862e+07 | 107657.780595 | POLYGON ((-87.63584 41.80905, -87.63585 41.809... | 89 | 1.071274e-06 | 5.0 |
| 2 | 3.0 | 53.0 | 2022-06-01 | 00:00:00.000 | 3.0 | {4F169974-0F93-4C35-98C1-20A8415C72F5} | 9.777341e+07 | 85625.597620 | POLYGON ((-87.61992 41.86577, -87.61985 41.865... | 101 | 1.033001e-06 | 0.0 |
| 27 | 28.0 | 78.0 | 2022-06-01 | 00:00:00.000 | 28.0 | {65BDECD2-AB03-4F1B-8025-49E3B1029A49} | 1.600439e+08 | 129592.820270 | POLYGON ((-87.72098 41.88802, -87.71974 41.888... | 158 | 9.872291e-07 | 6.0 |
| 26 | 27.0 | 77.0 | 2022-06-01 | 00:00:00.000 | 27.0 | {5EFBE372-72FB-4702-8C3C-BA882825EEF1} | 1.595750e+08 | 129446.885360 | POLYGON ((-87.63420 41.89660, -87.63426 41.896... | 155 | 9.713300e-07 | 2.0 |
| 15 | 16.0 | 66.0 | 2022-06-01 | 00:00:00.000 | 16.0 | {B54690B6-21DF-44F5-B898-D73EF9976BDC} | 1.258524e+08 | 90382.506038 | POLYGON ((-87.64077 41.77616, -87.64082 41.776... | 122 | 9.693898e-07 | 1.0 |
| 5 | 6.0 | 56.0 | 2022-06-01 | 00:00:00.000 | 6.0 | {FD74A999-4BBA-4CE3-BEBB-CC76423037E8} | 1.392022e+08 | 80779.851890 | POLYGON ((-87.61794 41.77292, -87.61786 41.772... | 134 | 9.626284e-07 | 3.0 |
| 35 | 36.0 | 86.0 | 2022-06-01 | 00:00:00.000 | 36.0 | {337BAF17-7684-4F7F-BFCA-0BFD2B92B869} | 8.383845e+07 | 118667.485047 | POLYGON ((-87.76599 41.92398, -87.76591 41.923... | 80 | 9.542162e-07 | 5.0 |
| 4 | 5.0 | 55.0 | 2022-06-01 | 00:00:00.000 | 5.0 | {0A109A41-9DED-47D7-934E-1EA1CC7EE025} | 1.120803e+08 | 88207.690241 | POLYGON ((-87.56030 41.76636, -87.56058 41.766... | 95 | 8.476069e-07 | 3.0 |
| 16 | 17.0 | 67.0 | 2022-06-01 | 00:00:00.000 | 17.0 | {A0475DAB-473E-447B-9F82-AEC0CA483C93} | 1.301704e+08 | 90154.781522 | POLYGON ((-87.65421 41.76869, -87.65421 41.768... | 110 | 8.450464e-07 | 5.0 |
| 20 | 21.0 | 71.0 | 2022-06-01 | 00:00:00.000 | 21.0 | {BA7C81E8-7DEB-46DF-8E71-95F9AE112FF3} | 2.118767e+08 | 92642.006031 | POLYGON ((-87.62405 41.73014, -87.62410 41.730... | 174 | 8.212322e-07 | 1.0 |
| 46 | 47.0 | 97.0 | 2022-06-01 | 00:00:00.000 | 47.0 | {4F17B913-08A1-4EF6-BF20-0EF4B6478494} | 8.481029e+07 | 55157.008247 | POLYGON ((-87.65979 41.97332, -87.65978 41.973... | 69 | 8.135805e-07 | 5.0 |
| 28 | 29.0 | 79.0 | 2022-06-01 | 00:00:00.000 | 29.0 | {C1A9937E-2864-4CFB-ADD5-B8397C56DC77} | 1.193660e+08 | 97816.079177 | POLYGON ((-87.80127 41.93944, -87.80127 41.939... | 96 | 8.042494e-07 | 3.0 |
| 42 | 43.0 | 93.0 | 2022-06-01 | 00:00:00.000 | 43.0 | {1003F8BC-CAE5-46E7-8A50-95CC5B60E8C7} | 6.223707e+07 | 48459.476947 | POLYGON ((-87.63183 41.93305, -87.63183 41.932... | 50 | 8.033797e-07 | 0.0 |
| 22 | 23.0 | 73.0 | 2022-06-01 | 00:00:00.000 | 23.0 | {51BB999E-306A-4091-8F7E-4241AACA3340} | 1.168305e+08 | 94602.226753 | POLYGON ((-87.71024 41.77156, -87.71039 41.771... | 91 | 7.789062e-07 | 2.0 |
| 47 | 48.0 | 98.0 | 2022-06-01 | 00:00:00.000 | 48.0 | {174DEC8E-5281-4774-831E-A7E0F6F3440D} | 4.500890e+07 | 35618.125393 | POLYGON ((-87.64430 41.96990, -87.64516 41.969... | 35 | 7.776239e-07 | 1.0 |
| 32 | 33.0 | 83.0 | 2022-06-01 | 00:00:00.000 | 33.0 | {F01E135A-54DB-49B2-867E-7F2DCD35A1F6} | 6.217322e+07 | 48801.435311 | POLYGON ((-87.70353 41.97186, -87.70344 41.971... | 48 | 7.720366e-07 | 2.0 |
| 19 | 20.0 | 70.0 | 2022-06-01 | 00:00:00.000 | 20.0 | {1EA93731-81AC-42D6-9711-283C3A3CA2BE} | 1.453499e+08 | 100221.251179 | POLYGON ((-87.62082 41.79383, -87.62050 41.793... | 112 | 7.705545e-07 | 5.0 |
| 25 | 26.0 | 76.0 | 2022-06-01 | 00:00:00.000 | 26.0 | {BB18C0C9-4A3A-43F8-A953-6A8B4D2487D5} | 7.767961e+07 | 60930.848090 | POLYGON ((-87.73649 41.92253, -87.73648 41.922... | 59 | 7.595301e-07 | 6.0 |
| 31 | 32.0 | 82.0 | 2022-06-01 | 00:00:00.000 | 32.0 | {2106D44D-07AC-4B39-A878-BEBB439D1BF3} | 9.288828e+07 | 47973.924224 | POLYGON ((-87.68097 41.94636, -87.68097 41.946... | 70 | 7.535934e-07 | 8.0 |
| 13 | 14.0 | 64.0 | 2022-06-01 | 00:00:00.000 | 14.0 | {612295DC-E128-468C-B0C9-A1ADE1CB1C84} | 1.213109e+08 | 72296.250663 | POLYGON ((-87.69421 41.80828, -87.69420 41.808... | 89 | 7.336524e-07 | 9.0 |
| 23 | 24.0 | 74.0 | 2022-06-01 | 00:00:00.000 | 24.0 | {9ED052F7-F703-4A2D-A829-363FB60472C4} | 1.120000e+08 | 68816.597232 | POLYGON ((-87.69070 41.85635, -87.69077 41.856... | 81 | 7.232144e-07 | 4.0 |
| 24 | 25.0 | 75.0 | 2022-06-01 | 00:00:00.000 | 25.0 | {B1A8F67E-3BEB-4E6A-967B-DB2271B162C5} | 9.329780e+07 | 80678.887175 | POLYGON ((-87.63901 41.86002, -87.63901 41.859... | 67 | 7.181306e-07 | 1.0 |
| 11 | 12.0 | 62.0 | 2022-06-01 | 00:00:00.000 | 12.0 | {EAD019CB-AA66-48C4-9338-795B4E083B59} | 1.334616e+08 | 76139.484398 | POLYGON ((-87.66588 41.84493, -87.66581 41.844... | 95 | 7.118152e-07 | 1.0 |
| 7 | 8.0 | 58.0 | 2022-06-01 | 00:00:00.000 | 8.0 | {2B5945BD-349C-4088-9B25-D99D4FB6C01D} | 1.799926e+08 | 77255.568224 | POLYGON ((-87.58587 41.76424, -87.58597 41.764... | 115 | 6.389150e-07 | 2.0 |
| 29 | 30.0 | 80.0 | 2022-06-01 | 00:00:00.000 | 30.0 | {15C33223-51B6-456F-B790-BEC795885C33} | 9.490456e+07 | 81414.586459 | POLYGON ((-87.73242 41.95181, -87.73242 41.951... | 58 | 6.111403e-07 | 5.0 |
| 48 | 49.0 | 99.0 | 2022-06-01 | 00:00:00.000 | 49.0 | {F7B2FFFC-D9B6-463F-BFF0-8C5C2ED72D47} | 5.088363e+07 | 37398.359952 | POLYGON ((-87.65729 42.00418, -87.65729 42.004... | 31 | 6.092332e-07 | 0.0 |
| 49 | 50.0 | 100.0 | 2022-06-01 | 00:00:00.000 | 50.0 | {C4F10402-10E2-4401-BD4D-7FBDAF35E3FC} | 7.450479e+07 | 60789.219509 | POLYGON ((-87.68189 42.00896, -87.68204 42.008... | 45 | 6.039880e-07 | 0.0 |
| 44 | 45.0 | 95.0 | 2022-06-01 | 00:00:00.000 | 45.0 | {18480250-3E08-4D68-BF2E-96E27F6503B5} | 1.394346e+08 | 117115.300022 | POLYGON ((-87.77310 42.01148, -87.77309 42.011... | 82 | 5.880892e-07 | 2.0 |
| 12 | 13.0 | 63.0 | 2022-06-01 | 00:00:00.000 | 13.0 | {883D9536-9DDE-4FB8-B340-E9A644FD1851} | 1.480001e+08 | 59975.600111 | POLYGON ((-87.74205 41.77113, -87.74206 41.771... | 87 | 5.878374e-07 | 0.0 |
| 43 | 44.0 | 94.0 | 2022-06-01 | 00:00:00.000 | 44.0 | {E181C590-6E28-4CF9-85A1-96AF19075184} | 4.750785e+07 | 36707.423435 | POLYGON ((-87.65682 41.95140, -87.65681 41.951... | 27 | 5.683272e-07 | 0.0 |
| 37 | 38.0 | 88.0 | 2022-06-01 | 00:00:00.000 | 38.0 | {6E5F0557-2DCA-490E-8E7D-C5B58CA65DFA} | 1.775660e+08 | 91571.573668 | POLYGON ((-87.75961 41.95327, -87.75964 41.953... | 98 | 5.519076e-07 | 7.0 |
| 18 | 19.0 | 69.0 | 2022-06-01 | 00:00:00.000 | 19.0 | {91001AF5-B4E0-4F45-A1CB-7C22070BA22D} | 2.199899e+08 | 101405.125517 | POLYGON ((-87.66674 41.73022, -87.66677 41.730... | 121 | 5.500252e-07 | 4.0 |
| 21 | 22.0 | 72.0 | 2022-06-01 | 00:00:00.000 | 22.0 | {0705D4DE-80BF-473A-B3B7-078D63AD0298} | 1.055096e+08 | 60413.987154 | POLYGON ((-87.70761 41.84995, -87.70761 41.849... | 58 | 5.497128e-07 | 0.0 |
| 6 | 7.0 | 57.0 | 2022-06-01 | 00:00:00.000 | 7.0 | {279FCBD9-EA0D-4FFC-A8CA-EDA2676C0721} | 1.414924e+08 | 98906.567862 | POLYGON ((-87.54393 41.76030, -87.54370 41.760... | 72 | 5.088612e-07 | 0.0 |
| 10 | 11.0 | 61.0 | 2022-06-01 | 00:00:00.000 | 11.0 | {C7397C87-C4AE-4C48-A00E-50D2CD1100CF} | 1.273484e+08 | 64972.869726 | POLYGON ((-87.63600 41.81635, -87.63602 41.816... | 60 | 4.711484e-07 | 1.0 |
| 17 | 18.0 | 68.0 | 2022-06-01 | 00:00:00.000 | 18.0 | {E22B6995-91F8-4C49-A9F3-4085D503F2A4} | 1.933789e+08 | 79337.751930 | POLYGON ((-87.65830 41.73950, -87.65834 41.739... | 85 | 4.395517e-07 | 10.0 |
| 45 | 46.0 | 96.0 | 2022-06-01 | 00:00:00.000 | 46.0 | {C894F602-BEBF-4984-803F-736F2BB8F6DE} | 5.592727e+07 | 52699.506856 | POLYGON ((-87.64521 41.96974, -87.64516 41.969... | 23 | 4.112484e-07 | 3.0 |
| 39 | 40.0 | 90.0 | 2022-06-01 | 00:00:00.000 | 40.0 | {EA278E33-45CB-4D28-BEE1-F64C155E65B9} | 9.664484e+07 | 67854.255079 | POLYGON ((-87.68152 42.00812, -87.68144 42.007... | 37 | 3.828451e-07 | 1.0 |
| 38 | 39.0 | 89.0 | 2022-06-01 | 00:00:00.000 | 39.0 | {15480A27-2480-40CF-91EC-72B090F03522} | 1.818672e+08 | 77704.757487 | POLYGON ((-87.69916 41.98312, -87.69915 41.982... | 62 | 3.409082e-07 | 8.0 |
| 8 | 9.0 | 59.0 | 2022-06-01 | 00:00:00.000 | 9.0 | {B75B90E6-CB95-4890-BBC3-D2F313E2EE80} | 2.344895e+08 | 102227.294779 | POLYGON ((-87.61414 41.72189, -87.61414 41.721... | 54 | 2.302875e-07 | 2.0 |
| 9 | 10.0 | 60.0 | 2022-06-01 | 00:00:00.000 | 10.0 | {19FF2026-6DDD-45DA-8DF6-B44B0C15834C} | 5.773395e+08 | 147455.404991 | POLYGON ((-87.53018 41.74310, -87.53008 41.742... | 80 | 1.385667e-07 | 4.0 |
| 40 | 41.0 | 91.0 | 2022-06-01 | 00:00:00.000 | 41.0 | {C885247A-E064-4FCF-A35A-747DEC142421} | 4.754967e+08 | 218605.417697 | POLYGON ((-87.79065 42.00040, -87.79058 42.000... | 62 | 1.303900e-07 | 1.0 |
x1=speedWards.cameras
y1=speedWards.speedcrashes_per_area
plt.scatter(x1,y1)
z1=np.polyfit(x1, y1, 1)
p1 = np.poly1d(z1)
plt.plot(x1,p1(x1),"r")
plt.xlabel("Speed Cameras")
plt.ylabel("Density of Speed Caused Crashes")
plt.show()
plt.savefig('SpeedCausedCrashCorrelation.png',dpi=300)
<Figure size 432x288 with 0 Axes>
#Finding the covariance
covariance1 = np.cov(x1, y1)
print(covariance1)
#Finding Pearson's correlation p-value
corr1, _ = pearsonr(x1,y1)
print('Pearsons correlation: %.3f' % corr1)
#Finding Spearmans correlation p-value, which is helpful for understanding nonlinear relationships
corr1, _ = spearmanr(x1, y1)
print('Spearmans correlation: %.3f' % corr1)
[[ 6.72816327e+00 -1.04108586e-07]
[-1.04108586e-07 3.14422055e-13]]
Pearsons correlation: -0.072
Spearmans correlation: 0.037
#The p-value changes, but is still insignificant.
#Analyzing this data by ward could be problematic, but is still important since alderman have control over a lot of traffic changes in their wards.
#plot the speed cameras against the crash points as a base map. This gives us a little more granular view.
#A more granular view is important because speed cameras are extremely localized. Usually, they only capture one direction of traffic on one street in one intersection.
base=TrafficCrashes.plot(markersize=1, color='silver', figsize=(20,16))
Speedcam_crashes_by_ward.plot(ax=base,figsize=(14,12),marker='P', color='blue', markersize=40, edgecolor='lightblue')
<AxesSubplot:>
With my previous methods of mapping, it is still too difficult to see the details of this data. Below, I begin to use folium so I can interact with the map and gain more insights.
Wards.dtypes
ward object
objectid float64
date_edit_ object
time_edit_ object
ward_id float64
globalid object
st_area_sh float64
st_length_ float64
geometry geometry
number_of_crashes int64
crashes_per_area float64
cameras float64
dtype: object
#additional libraries need to be imported for this method.
#I will similarily utilize the choropleth map as the first map layer and plot the speed camera locations on top.
#The main benefits of this method is that I can interact with the map, zoom in and out, and use a basemap that have detailed street geography.
#This makes it easier for me to look at street intersections, highway locations, parks, etc.
import json
import folium
#we must convert the date column to a string because a date field cannot be converted to geojson
Speedcam_crashes_by_ward['GO-LIVE DATE']=Speedcam_crashes_by_ward['GO-LIVE DATE'].astype(str)
speedcam_gjson = folium.features.GeoJson(Speedcam_crashes_by_ward,
name = "Speed Camera Locations",
marker=folium.CircleMarker(radius=7,fill_color="grey", fill_opacity=0.7, color="black",weight=3),
tooltip=folium.GeoJsonTooltip(fields=['ADDRESS','GO-LIVE DATE','FIRST APPROACH','SECOND APPROACH','ward']))
fmap = folium.Map(location=[41.8,-87.6], zoom_start=11)
folium.Choropleth(
geo_data= Wards,
name="choropleth",
data=Wards,
columns = ["ward", "crashes_per_area"],
key_on="feature.properties.ward",
fill_color="YlOrBr",
fill_opacity=0.7,
line_opacity=0.2,
legend_name="Crash Density",).add_to(fmap)
speedcam_gjson.add_to(fmap)
folium.LayerControl().add_to(fmap)
fmap
Make this Notebook Trusted to load map: File -> Trust Notebook
#Taking this method a step further, I decide to get rid of the ward boundaires and look specifically as concentrated ares of crashes.
#In order to do this, I will utilize a folium plug in that takes point data (crash data in my map) and transforms it into a heat map.
#I plot the speed cameras on top.
#Now, I am able to look at the speed camera locations and determine if there are concentrations of crashes nearby.
#This type of map can also be important for determining locations for new speed cameras to improve safety.
from folium import plugins
heatmap = folium.Map(location = [41.922446, -87.674360], zoom_start=14.8)
heat_data = [[point.xy[1][0], point.xy[0][0]] for point in TrafficCrashes.geometry]
heat_data
speedcam_gjson.add_to(heatmap)
plugins.HeatMap(heat_data).add_to(heatmap)
heatmap.save('heatmap.html')
heatmap
Make this Notebook Trusted to load map: File -> Trust Notebook
#repeating these steps with the data that only includes speed caused crashes
heatmap2 = folium.Map(location = [41.922446, -87.674360], zoom_start=14.8)
heat_data2 = [[point.xy[1][0], point.xy[0][0]] for point in SpeedCrashes.geometry]
heat_data2
speedcam_gjson.add_to(heatmap2)
plugins.HeatMap(heat_data2).add_to(heatmap2)
heatmap2
Make this Notebook Trusted to load map: File -> Trust Notebook
Using Folium allows me to investigate certain areas and hotspots.
Zooming in and out of the data allows me to see target areas for intervention.
The map showing only speed related crashes helps show that the speed cameras are effective in preventing certain types of crashes in certain areas.